Recognition of Russian traffic signs in winter conditions. Solutions ofthe “Ice Vision” competition winners.

Artem L. Pavlov1, Azat Davletshin2, Alexey Kharlamov3, Maksim S. Koriukin4, Artem Vasenin2, Pavel Solovev3,Pavel Ostyakov3, Pavel A. Karpyshev1, George V. Ovchinnikov5, Ivan V. Oseledets6, and Dzmitry Tsetserukou6

Abstract— With the advancements of various autonomous carprojects aiming to achieve SAE Level 5, real-time detection oftraffic signs in real-life scenarios has become a highly relevantproblem for the industry. Even though a great progress hasbeen achieved in this field, there is still no clear consensus onwhat the state-of-the-art in this field is.

Moreover, it is important to develop and test systems invarious regions and conditions. This is why the “Ice Vision”competition has focused on the detection of Russian trafficsigns in winter conditions. The IceVisionSet dataset used forthis competition features real-world collection of lossless framesequences with traffic sign annotations. The sequences werecollected in varying conditions, including: different weather,camera exposure, illumination and moving speeds.

In this work we describe the competition and present thesolutions of the 3 top teams.

I. INTRODUCTION

The recognition of traffic signs is a multi-category clas-sification problem with unbalanced class frequencies. It is ahighly relevant industrial problem, with autonomous drivingbeing the most prominent application of the technology.While being simpler than many other computer vision prob-lems due to the availability of reference images, it hassignificant reliability requirements, since false detectionsmay cause incorrect behavior of an autonomous car, whichmay end in a traffic accident.

Additionally, while traffic signs show a wide range ofvariations between classes in terms of color, shape, and thepresence of pictograms or text, some classes contain impor-tant information which must be recognized for sign detectionto be useful, e.g. speed limit or distance information. Othersigns change their meaning depending on orientation, e.g.pedestrian crossing or turn left/right signs. In other words,subtle differences in signs can have a significant impact ondecision making.

1A. L. Pavlov and P. A. Karpyshev are PhD students with the SpaceCenter, Skolkovo Institute of Science and Technology (Skoltech), Moscow,Russia. artem.pavlov@skolkovotech.ru

2A. Davletshin and A. Vasenin are ML Engineers with the NtechLabcompany. a.davletshin@ntechlab.com

3A. Kharlamov, P. Solovev and P. Ostyakov are Research Scientists withthe Samsung AI Center, Moscow.

4M. S. Koriukin is a PhD student with the Department of System Anal-ysis, Institute of Computer Science and Telecommunications, ReshetnevSiberian State University of Science and Technology, Krasnoyarsk, Russia.

5G. V. Ovchinnikov is a Research Scientist with the Center for Compu-tational and Data-Intensive Science and Engineering (CDISE), Skoltech.

6I. V. Oseledets and D. Tsetserukou are Full and Associate Professorswith the CDISE and the Space Center respectively, Skoltech.

Fig. 1: Frame examples from the IceVisionSet dataset.

Fig. 2: Annotated sign examples from the IceVisionSetdataset.

Moreover, algorithms have to cope with large variationsin visual appearance due to illumination changes, partialocclusions, rotations, weather conditions, scaling, etc. Andthey have to deal with a large number of highly unbalancedclasses, e.g. in Russia there are almost 300 sign classes, notcounting variations inside class (i.e. speed limit is countedas a single class). Plus the number of classes rises fast ifsolution is intended to be used in many countries, most ofwhich have different traffic signs.

According to the survey by Paclik [1], the works on trafficsign detection and recognition started as early as in 1984. Agreat deal of excellent algorithms for traffic sign detectionwas proposed since then. Detailed reviews on this topic canbe found in [2] and [3].

Deep learning approaches are overwhelmingly popularamongst the recent works in this field. Having large datasetsis of the utmost importance for training such solutions. Agreat number of traffic sign datasets for different coun-tries has been previously published, including: the BelgianTraffic Sign Dataset [4], German Traffic Sign Recognition

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Benchmark [5], German Traffic Sign Detection Benchmark[6], Swedish traffic sign dataset [7], Chinese Traffic SignDatabase1 and Russian Traffic Sign Dataset (RTSD) [8].

One of the most recent traffic signs datasets is IceVisionSet[9]. It focuses on Russian winter roads and covers bothday and night conditions. Contrary to many datasets, itprovides sequences of raw Color Filter Array (CFA) framescompressed using lossless algorithms. This dataset was usedfor the “Ice Vision” competition, the final stage of whichwas held in July 2019.

In this paper we present a short description of the compe-tition and the solutions of the 3 top teams:

• First place: Azat and Artem (Azat Davletshin, ArtemVasenin).

• Second place: PsinaDriveNow (Alexey Harlamov, PavelSolovev, Pavel Ostyakov).

• Third place: Vizorlabs (Maksim S. Koriukin).

II. THE “ICE VISION” COMPETITION

The competition was organized by the Russian VentureCompany in association with the National University of Sci-ence and Technology MISiS, Skolkovo Institute of Scienceand Technology , and the “Starline” company. It was dividedinto 2 stages: online and offline. The first stage was usedto select teams which will participate in the second one.Additionally, several foreign teams have been invited to theoffline stage, namely: the Harbin Institute of Technology, thePolitechnical University of Catalonia, the University of Paris-Saclay, the University of Science and Technology Beijing,the University of WisconsinStout, and the University ofUlsan.

The competition was based on the IceVisionSet dataset.The annotations had two distinct parts. The first part con-tained frame-by-frame annotations with linearly interpolatedbounding boxes created using Computer Vision AnnotationTool (CVAT)2, this part was presented in [9]. The second partwas annotated after the paper submission. It was done in theSupervisely web-tool, and had only approximately each 30thframe annotated (we have used a random step between 25and 35 frames, so participants will not know which framesare annotated), but all annotations were manual.

Both dataset images and annotations are published un-der open CC BY 4.0 license and can be downloadedfrom http://oscar.skoltech.ru and https://github.com/icevision/annotations respectively.You can find annotation statistics behind the second link.

For the competition an IoU-based metric was used. Bound-ing boxes with an area smaller than 100 pixels were ignoredduring evaluation. The final result was a sum of pointsfor each correctly detected sign minus penalties for false-positive detections (2 points for each such detection). If asign was detected twice, then the detection with the smallestIoU was counted as a false positive. Scoring was handled

1http://www.nlpr.ia.ac.cn/pal/trafficdata/index.html

2https://github.com/opencv/cvat

using a scoring software with an open source code, see:https://github.com/icevision/score.

During the online stage the participants had to detect only10 sign classes. See table in the README v0.1.53. Detectionwas considered successful if IoU was bigger or equal to 0.5and the bounding box had a correct class code. If IoU ofa true positive detection was bigger than 0.85, it resultedin adding 1 point to final result. Otherwise the points werecalculated as ((IoU − 0.5)/0.35)0.25.

During the offline stage the participants had to detect allsigns defined by the Russian traffic code. Detection wasconsidered successful if IoU was bigger or equal to 0.3 andthe bounding box had a correct class or superclass code.Score for true positives was calculated as (1 + k1 + k2 +k3) ∗ s, where: s is the “base” score, k1 is a coefficientfor detecting sign code, k2 is a coefficient for detectingassociated data, k3 is a coefficient for detecting temporarysign. If (1 + k1 + k2 + k3) < 0, the detection score was setto 0. If IoU > 0.85, s = 1. Otherwise it is calculated usingthe following equation: ((IoU0.3)/0.55)0.25. For a completedescription of coefficients computation please refer to theREADME.

Additionally, the participants were constrained by compu-tational power and time. During the offline stage they hadto process 100 000 frames ( 50 000 stereo-pairs) in 5 hoursusing a virtual machine with a single NVIDIA Tesla V100and 8 core CPU, provided by Zhores Cluster4.

All participant’s submissions from the online and of-fline stages are published under open CC BY 4.0 licenseand can be downloaded from: https://github.com/icevision/solutions.

III. THE FIRST PLACE SOLUTION

A. Algorithm overview

The team used a “detect and track” approach for theirfinal solution. The detector architecture was Faster R-CNN[10] with FPN[11], Cascade Head [12] and DeformableConvolutions[13]. IoU-Tracker[14] was used for tracking.Each track was further refined by combining class proba-bilities from all bounding boxes belonging to a track andselecting the best one. For each track that was classified asa speed limit sign, the team also predicted the speed limitvalue.

The train set of the competition was relatively small,therefore the team pre-trained the detector on COCO[15] andRTSD[8].

The code of the final solution can be found at:https://gitlab.com/rizhiy/ice vision.

B. Detection

The team used MMdetection[16] as the base frameworkfor their detection pipeline. The baseline configuration wasFaster R-CNN + FPN, pre-trained on ImageNet[17]. Sincethe challenge had performance and time restrictions, the team

3https://github.com/icevision/score/tree/v0.1.54https://www.zhores.net/

Fig. 3: Part of the “Ice Vision” leaders table available at https://visiontest.misis.ru. Green numbers denote score for successfuldetections and red numbers denote penalties for incorrect detections.

decided to use ResNet-50[18] as a backbone to balance speedand accuracy.

During the challenge, the team has performed severalexperiments to see which approaches perform well on thecompetition dataset. For experiments evaluation the standardCOCO mAP metric was used.

As a starting point the team has used a model pre-trained on the COCO dataset provided by the MMdetectionframework. Next model was trained on RTSD, this datasetalso contains Russian traffic sign annotations for videoscaptured from a moving car and is bigger than the trainingdataset used in the competition. Finally the resulting modelwas fine-tuned on the training data provided by organizers.The final training pipeline was as follows: COCO → RTSD→ IceVision.

The metric used in this competition awarded higher scoreto more precise detections (higher IoU), therefore the teammade an experiment using Cascade Head. Cascade Head usesmultiple stages to refine proposals produced by the FPN,each trained with a higher IoU threshold. This approachproduces more precise detections.

While traffic signs have pretty defined shape, when cap-tured from a car they can appear squashed and twisted. Toease the amount of work the classifier has to do, the teamused Deformable convolutions which can bring features intoa unified shape.

Multi-scale training augmentation was used, as it almostalways helps with lack of data.

Many of the signs in the dataset were small (with anarea less than 300 pixels). JPEG compression negativelyimpacts classification on small signs. By using original rawBayer PNM files provided by the organizers and using bi-linear demosaicing to calculate RGB image expected by thebackbone, the team was able to improve classification onsmall signs.

The final configuration achieved COCO mmAP score of0.412 on the validation set.

C. Tracking

Running detector on each frame at good resolution wasnot feasible because of the computation power and timingconstraints. Therefore the team has decided to run thedetector once every three frames, track the detections andlinearly interpolate the bounding boxes in-between. To trackthe bounding boxes the team used IoU tracker.

IoU tracker works as follows: For each track, IoU iscalculated for all detections in a new frame. If the highestIoU is higher than the threshold, that detection is added tothe track. All detections which are not assigned to a track,start as a new one.

During testing the team found that signs can move quite alot even in three frames, but are located sparsely. Therefore,lower IoU thresholds produced better tracks. In final solutionthe team used IoU threshold of 0.1.

D. Track Refinement

Usage of a tracker also allowed to improve the quality ofdetections. In the video the same signs can be seen multipletimes at various distances. By tracking it, the team was ableto use predictions from multiple frames. Predictions wereaveraged and the best one was assigned to all detections inthe track.

Additionally, since competition allowed predicting super-categories, the team added simple logic to combine classprobabilities. The logic was as follows: if none of the mostspecific class probabilities were above the threshold, classprobabilities of each 2nd-level-category were summed up.Each probability was again checked if it was above thethreshold and if not, probabilities for top-level-categorieswould be summed up and again checked against the thresh-old.

The team used different thresholds for each categorylevel. Thresholds themselves were found using grid-searchon validation set.

Finally, if a sign was classified as a speed limit sign, thebounding box was cut out from the original image and the

number on the sign was predicted using a ResNet-34.Since the metric used in the competition summed, rather

than averaged, scores of each class, therefore the team didnot attempt to fix class imbalance.

IV. THE SECOND PLACE SOLUTION

The team used Cascade R-CNN [19] with the ResNeXt101[20] backbone as a base architecture. Training was doneusing the MMDetection framework [16] in 2 stages:

1) Training of the full model (head and backbone) usingRTSD dataset [8].

2) Fine-tuning using IceVisionSet images after demosaic-ing, cropping of the lower 600 rows and resizing to1632x966 pixels.

Due to the timing restrictions, ResNext101 features werecomputed only for each 5th frame. Bounding box posi-tions for in-between frames were interpolated using cross-correlation metric.

Source code of the final solution can be downloaded fromhttps://github.com/gamers5a/SignDetection.

A. Interpolation

Matching of bounding boxes between keyframes wasdone using normalized cross-correlation between sub-imagesselected by bounding boxes, i.e. without taking classificationresults into account. After a pair of bounding boxes havebeen found, a search area is found by adding/subtracting20 pixels from maximum/minimum coordinates of boundingboxes (see Fig. 5).

The size of an in-between bounding box was calculatedusing linear interpolation, while position was found usingmaximum cross-correlation response in the search area.

B. Signs classification

One of the tasks of the competition was the recognition oftext on signs (city signs, numerical values of the applicabilityof the signs. Since solving the OCR problem by tailoringthe conditions of entries provided by the organizers requiresa considerable amount of time, the team has decided toapproach the solution of the problem from the other side,namely, to solve the problem of classifying text values ratherthan recognition.

One of the features was the inability to directly use flipsduring training, since there are signs of pedestrian crossingsin different directions in the training, the rotation of whichchanged the annotation to the wrong one, which is whyduring the training the team first used flips, and in thevery last era it was turned off for network to learn correctorientation of a sign.

C. Inference speed optimization

To accelerate the inference, the team used Float16, whichis supported by the MMDetection framework, while trainingwas done using Float32. This approach allowed to improvethe network performance from 2 to 3 fps on the originalpictures. During dataset exploration, the team has noted thatthe camera is installed on the machine in a such way that

signs almost never appear in the lower part of frames, whichis expected since signs are installed to improve visibility fordrivers. Thus the team has ignored lower 600 out of 2048rows, it improved detector performance by additional 30%.

Since the final testing assumed a large load on the filesystem implemented using IBM GPFS, the team has decidedto reduce the use of random reading from a remote filestorage by asynchronously caching video sequences to RAM,in parallel with their prediction, while the GPU and diskload were further balanced using the Round-Robin algorithm.Since the the team solution required 3 independent passesthrough video sequences (a purely sequential reading ofwhich would take more time than was allocated for predict-ing the entire test suite), this approach allowed the team toget rid of data loading problems.

D. Domain adaptation

The dataset contains both day and night sequences. Nightsequences were under-exposed in some parts to limit blurcaused by the car movement. This is why the team hasused histogram equalization filter incorporated into the frameconversion tool provided by the organizers. The histogramequalization filter boost image contrast, but amplifies noise,degrades colors and changes background brightness depend-ing on the amount of bright objects in the frame.

This is why predictions on night data are less accurate thanday data. To improve the results on night sequences, the teamhas tried to increase dataset diversity by applying DomainAdaptation of day sequences to night ones. To do this, theteam has trained MUNIT [21], to translate day frames tonight ones and vice versa. The reverse transformation turnedout to be of rather poor quality, because night images simplydo not have all the necessary information to restore theimage, so only day to night conversions were added to thetraining dataset.

V. THE THIRD PLACE SOLUTION

Most of the existing methods for object detection andclassification can be roughly divided into three main stages:proposal of a candidate object from the region, selectionof signs and classification. In order to establish the regionsthat are likely to contain objects of interest, many methodsuse the sliding window search strategy[22]. These methodsuse windows with different scaling and ratio to scan theimage with a fixed pitch size. An algorithm called “Selectivesearch” was proposed by Uuijlings et al.[23] to establishpossible locations of target objects. During the selectivesearch, one starts with each individual pixel as its own group.Then, one calculates the texture for each group and combinesthe two textures that are the closest. The algorithm continuesto merge the regions until all groups are clustered.

These methods are widely used with deep convolutionalnetworks for object detection. In [24], the region proposalnetwork (RPN) was offered, as well as the new neuralnetwork architecture for objection detection named R-CNN.The region proposal method (RPN) subsequently becamethe most popular method for proposing the region for the

Fig. 4: Detection pipeline used by the PsinaDriveNow team. “B” stands for “bounding box”, “C” for classification resultand RPN for Region Proposal Network.

Fig. 5: Interpolation pipeline used by the PsinaDriveNow team.

object of interest. The R-CNN architecture requires manyregion proposals to be precise, but many regions overlapwith each other. Instead of extracting the functions for eachimage patch from scratch, the functions extractor based onthe convolutional neural network can be used [10]. The teamhas also used an external region proposal method (selectivesearch), to establish the areas of interest that are subsequentlycombined with relevant object maps to obtain corrections forobject detection.

The object detection methods which follow this approachare well known as two-stage methods: proposal of the regioncandidate at the first stage and object classification at thesecond one. The two-step methods based on convolutionalnetworks achieve high accuracy on object detection tasks,but have a very low image processing speed. Methods whichdo not require additional operations region proposals, suchas YOLO [25] and SSD [26], represent so-called one-stepmethods. They are much faster than the two-step methods,but have lower accuracy. In particular, this trade-off makesthem unsuitable for detection of large number of smallobjects, which were common in the competition dataset.

A. Implementation

Large datasets is a key factor for training well-functioningCNN-based architectures that have millions of parameters.

Fig. 6: Pipeline used by the VizorLabs team.

In the past, some well-known datasets for various tasks havebeen published, for example, ImageNet [27].

Due to the small amount of available training data, itwas decided to enhance it by augmenting it with syntheticdata. The figure below shows the general arrangement forthe solution applied.

The team used Cascade R-CNN as a neural networkarchitecture with usage of the transfer learning method [28]using the pre-trained ResNetXt-101 [20] on the COCOdataset [15], and then set up the model for the IceVisiondataset. ResNetXt-101 CNN was used as a backbone. Adam[29] was used as an optimizer of the objective error function,with a training rate of 5e-3. While preparing the data for

(a) Smaller than 30x30 (b) Bigger than 30x30

Fig. 7: Spatial distribution of traffic signs in the IceVisionSetdataset for different sign sizes.

training, the team have used the Random Image Croppingapproach. Each batch of images included one fragment withthe dimensions of 2448 1700. The decision to use only partof the image for training was made upon viewing the spatialdistribution of traffic signs on all images (see Fig. 7). Objectsin a lower half of the frames were extremely rare.

VI. CONCLUSIONS

In this work we have presented the “Ice Vision” competi-tion focused on real-time detection of Russian traffic signs inwinter conditions. The IceVisionSet used for the competitionfeatures lossless real world data collected on Russian publicroads in different conditions, including weather and illumina-tion. During the competition strong timing restrictions wereenforced upon participants.

This work covers 3 solutions of the competition winners.Two teams have published the source code of their solutions.

We can observe that solutions share some similarities,common for modern deep learning architectures, i.e. solu-tions use backbone/head architecture and heavily rely ontransfer learning, detectors work with single images with-out utilizing temporal information outside of specializedalgorithm-based trackers. Probably due to the reliance ontransfer learning solutions have not used raw Bayer imagesand stereo information.

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