{"title":"交通灯检测的轻量级深度学习模型","authors":"S. Bali, T. Kumar, S. S. Tyagi","doi":"10.1109/ICTACS56270.2022.9988178","DOIUrl":null,"url":null,"abstract":"Traffic Light Detection is one among the vital applications of Advanced Driving Assistant Systems (ADAS). The objective of traffic light detection is classification and localization of traffic lights present in the real-time images. State of art research outlines that the deep learning models have achieved better results than the traditional methods in detecting the traffic lights but still the techniques suffer from problems of low accuracy, slow speed, small object detection, illumination variations and occluded objects. In this paper Darknet19 feature extractor of You Only Look Once (YOLO) version 2 is replaced with SqueezeNet pretrained model. The. To improve the detection results, K-means clustering algorithm is applied for finding the clusters of bounding boxes of subset of LaRA traffic light dataset that comprises of green and red traffic lights and seven different sized anchor boxes were chosen for experimentation. Data augmentation techniques are also applied for increasing the diversity of the dataset. The proposed model attained mean average precision (mAP@.50) score of 84% and the visual results on the test dataset showed that the traffic lights were detected with increased confidence score with the proposed model in comparison with MobileNetV2, ResNet50 as backbones of the original YOLOv2 model.","PeriodicalId":385163,"journal":{"name":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","volume":"109 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Lightweight Deep Learning Model for Traffic Light Detection\",\"authors\":\"S. Bali, T. Kumar, S. S. Tyagi\",\"doi\":\"10.1109/ICTACS56270.2022.9988178\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Traffic Light Detection is one among the vital applications of Advanced Driving Assistant Systems (ADAS). The objective of traffic light detection is classification and localization of traffic lights present in the real-time images. State of art research outlines that the deep learning models have achieved better results than the traditional methods in detecting the traffic lights but still the techniques suffer from problems of low accuracy, slow speed, small object detection, illumination variations and occluded objects. In this paper Darknet19 feature extractor of You Only Look Once (YOLO) version 2 is replaced with SqueezeNet pretrained model. The. To improve the detection results, K-means clustering algorithm is applied for finding the clusters of bounding boxes of subset of LaRA traffic light dataset that comprises of green and red traffic lights and seven different sized anchor boxes were chosen for experimentation. Data augmentation techniques are also applied for increasing the diversity of the dataset. The proposed model attained mean average precision (mAP@.50) score of 84% and the visual results on the test dataset showed that the traffic lights were detected with increased confidence score with the proposed model in comparison with MobileNetV2, ResNet50 as backbones of the original YOLOv2 model.\",\"PeriodicalId\":385163,\"journal\":{\"name\":\"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)\",\"volume\":\"109 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTACS56270.2022.9988178\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTACS56270.2022.9988178","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
交通信号灯检测是高级驾驶辅助系统(ADAS)的重要应用之一。红绿灯检测的目的是对实时图像中存在的红绿灯进行分类和定位。目前的研究表明,深度学习模型在交通信号灯检测方面取得了比传统方法更好的效果,但仍然存在精度低、速度慢、物体检测小、光照变化和遮挡物体等问题。本文用SqueezeNet预训练模型代替了You Only Look Once (YOLO) version 2的Darknet19特征提取器。的。为了提高检测结果,采用K-means聚类算法对LaRA红绿灯数据集子集的边界盒进行聚类,选取7个不同大小的锚盒进行实验。数据增强技术也被用于增加数据集的多样性。该模型的平均精度(mAP@.50)得分为84%,测试数据集上的视觉结果表明,与作为原始YOLOv2模型主干的MobileNetV2、ResNet50相比,该模型的交通灯检测置信度得分更高。
Lightweight Deep Learning Model for Traffic Light Detection
Traffic Light Detection is one among the vital applications of Advanced Driving Assistant Systems (ADAS). The objective of traffic light detection is classification and localization of traffic lights present in the real-time images. State of art research outlines that the deep learning models have achieved better results than the traditional methods in detecting the traffic lights but still the techniques suffer from problems of low accuracy, slow speed, small object detection, illumination variations and occluded objects. In this paper Darknet19 feature extractor of You Only Look Once (YOLO) version 2 is replaced with SqueezeNet pretrained model. The. To improve the detection results, K-means clustering algorithm is applied for finding the clusters of bounding boxes of subset of LaRA traffic light dataset that comprises of green and red traffic lights and seven different sized anchor boxes were chosen for experimentation. Data augmentation techniques are also applied for increasing the diversity of the dataset. The proposed model attained mean average precision (mAP@.50) score of 84% and the visual results on the test dataset showed that the traffic lights were detected with increased confidence score with the proposed model in comparison with MobileNetV2, ResNet50 as backbones of the original YOLOv2 model.