Volume 40 Issue 5
Nov.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(5): 112-119
ZHOU Yong, ZHANG Bingzhen, ZHANG Xiaoyong, LIU Yuming. A Detection Method for Abandoned Materials on Road Surface Based on an Improved YOLO and Background Differencing Algorithm[J]. Journal of Transport Information and Safety, 2022, 40(5): 112-119. doi: 10.3963/j.jssn.1674-4861.2022.05.012
Citation: ZHOU Yong, ZHANG Bingzhen, ZHANG Xiaoyong, LIU Yuming. A Detection Method for Abandoned Materials on Road Surface Based on an Improved YOLO and Background Differencing Algorithm[J]. Journal of Transport Information and Safety, 2022, 40(5): 112-119. doi: 10.3963/j.jssn.1674-4861.2022.05.012
shu

A Detection Method for Abandoned Materials on Road Surface Based on an Improved YOLO and Background Differencing Algorithm

doi: 10.3963/j.jssn.1674-4861.2022.05.012

  • Shenzhen Urban Transport Planning Center Co., Ltd, Shenzhen 518000, Guangdong, China

  • Received Date: 2022-03-02
    Available Online: 2022-12-05
    Abstract
  • Due to the problems such as low detection accuracy, limited types of materials that can be detected, and slow speed of detection algorithms for abandoned materials, a detection algorithm combining target detection based on deep learning and traditional image processing is proposed. The structure of the YOLOv5s detection algorithm is modified, in order to have a capacity of real-time detection. The downsampling module in YOLO is optimized using convolution; the original feature extraction network is replaced with a Ghost network to reduce the computational burden, and the anchor frame is designed to match the dataset according to the characteristics of the detected objects to improve the detection accuracy. The optimized YOLO algorithm is used to detect vehicles and pedestrians as traffic participants in the road scenes and the region of interest is set based on the detection results. By detecting foreground targets in the region of interest with a background differencing algorithm, and calculating the intersection and merger ratio between the foreground target and the detection results from the YOLO algorithm, the detection of road abandoned object can be completed after excluding the detected traffic participants. In the experiments of target detection, the improved YOLO algorithm has a detection speed of 20.67 ms for each frame without any drop in the detection accuracy, which is 16.42% faster than that of the original YOLO detection algorithm. Experimental results indicate that the mean average precision (mAP) of the traditional mixed Gaussian model algorithm is 0.51, while the mAP of the detection algorithm using the improved YOLO and background differencing is 0.78. The detection accuracy of the algorithm improves by 52.9%. The improved algorithm can be applied to scenarios where there is no data or sample data is limited. The detection time required for each frame is only 24.4 ms when the proposed algorithm is installed on a Jetson Xavier NX computer, and therefore it can be used to carry out real-time detection of abandoned materials.

     

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