基于改进YOLOX的轻量化交通监控目标检测算法

doi:10.3778/j.issn.1002-8331.2308-0081

摘要/Abstract

摘要： 交通目标检测技术是道路交通管理部门进行交通流量监测、安全管控等核心工作的重要技术之一。面对大量的交通监控视频数据，需要使用检测速度更快、精度更高、占用计算资源更少的交通目标检测技术。为了满足这一需求，根据YOLOX算法和PP-LCNet网络，提出了一种面向交通监控场景的轻量型交通目标检测算法PL-YOLO。使用基于PP-LCNet改进的网络作为目标检测器的主干特征网络，使用深度可分离卷积代替YOLOX中的普通卷积，降低模型运算过程中的复杂度；根据交通监控场景下的车辆分布密集且尺寸小的特点，添加SimAM注意力机制模块，聚焦于更有意义的特征图像。实验结果表明，相对于YOLOX-s模型，改进后的PL-YOLO检测精度提升1.89个百分点，模型大小降低了54%，FPS从20.88?帧/s提升到26.68?帧/s。

关键词: 目标检测, 交通监控场景检测, YOLOX, 轻量化, PP-LCNet

Abstract: Traffic target detection technology is an important tool for traffic management departments in key tasks such as traffic monitoring and safety surveillance. Faced with the large amount of traffic monitoring scene data, there is a need to employ traffic target detection techniques that offer fast detection speed, high accuracy and low computational resource utilization. To meet this need, this paper proposes a lightweight traffic target detection algorithm PL-YOLO for traffic monitoring scenes based on the YOLOX algorithm and the PP-LCNet network. Furthermore, considering the dense distribution and small size of vehicles in traffic monitoring scenes, the SimAM attention mechanism module is added to focus on more meaningful features. Experimental results demonstrate that PL-YOLO achieves 1.89 percentage points increase in detection accuracy, the model size decreases by 54% and the FPS increases from 20.88 frame/s to 26.68 frame/s compared to the YOLOX-s model.

Key words: object detection, traffic monitoring scene detection, YOLOX, lightweight, PP-LCNet

胡伟超, 郭宇阳, 张奇, 陈艳艳. 基于改进YOLOX的轻量化交通监控目标检测算法[J]. 计算机工程与应用, 2024, 60(7): 167-174.

HU Weichao, GUO Yuyang, ZHANG Qi, CHEN Yanyan. Lightweight Traffic Monitoring Object Detection Algorithm Based on Improved YOLOX[J]. Computer Engineering and Applications, 2024, 60(7): 167-174.

参考文献

[1] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, the Netherlands, 2016: 21-37.
[2] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2980-2988.
[3] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779-788.
[4] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 7263-7271.
[5] FARHADI A, REDMON J. YOLOv3: an incremental improvement[J]. arXiv:1804.02767, 2018.
[6] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.
[7] CAI Z, VASCONCELOS N. Cascade R-CNN: delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 6154-6162.
[8] LUO Q, WANG J, GAO M, et al. G-YOLOX: a lightweight network for detecting vehicle types[J]. Journal of Sensors, 2022, 2022(3): 1-10.
[9] LI Y, WANG J, HUANG J, et al. Research on deep learning automatic vehicle recognition algorithm based on RES-YOLO model[J]. Sensors, 2022, 22(10): 3783.
[10] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 770-778.
[11] DONG X, YAN S, DUAN C. A lightweight vehicles detection network model based on YOLOv5[J]. Engineering Applications of Artificial Intelligence, 2022, 113: 104914.
[12] GOMAA A, MINEMATSU T, ABDELWAHAB M M, et al. Faster CNN-based vehicle detection and counting strategy for fixed camera scenes[J]. Multimedia Tools and Applications, 2022, 81(18): 25443-25471.
[13] GEIGER A, LENZ P, STILLER C, et al. Vision meets robotics: the KITTI dataset[J]. The International Journal of Robotics Research, 2013, 32(11): 1231-1237.
[14] YU H, LUO Y, SHU M, et al. DAIR-V2X: a large-scale dataset for vehicle-infrastructure cooperative 3D object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 21361-21370.
[15] GE Z, LIU S, WANG F, et al. YOLOX: exceeding YOLO series in 2021[J]. arXiv:2107.08430, 2021.
[16] CUI C, GAO T, WEI S, et al. PP-LCNet: a lightweight CPU convolutional neural network[J]. arXiv:2109.15099, 2021.
[17] YANG L, ZHANG R Y, LI L, et al. SimAM: a simple, parameter-free attention module for convolutional neural networks[C]//Proceedings of the International Conference on Machine Learning Research, 2021: 11863-11874.
[18] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 7464-7475.