With the development of the economy and the improvement of people's living standards, the volume of goods transported is constantly on the rise. At present, the dominant position in the transport industry is still road transport, and at the same time the problem of road traffic accidents is becoming increasingly prominent. Generally speaking, the truck is relatively large, with a wide blind spot and high cab height, which makes the driver unable to have a good observation and control of the environment around the vehicle, so it is extremely easy to have all kinds of traffic accidents. In such a context, an improved Faster R-CNN algorithm is proposed for the detection of abnormal targets in the truck driving environment. First, the ResNet-50 network is chosen to replace the VGG network, which reduces the training difficulty and effectively improves the gradient disappearance problem; then, in order to enhance the feature extraction ability of the network, the Squeeze-and-Excitation attention mechanism is introduced in the residual structure to strengthen the feature extraction ability; finally, the original feature pyramid network structure is improved by adding a self Finally, the original feature pyramid structure was improved by adding a bottom-up channel enhancement route to improve the propagation of lower-level features and further enhance the propagation of feature information. The experimental results show that the improved mAP value improves by 2.05% compared to the original algorithm.

Long Li. Improved Faster R-CNN-Based Anomaly Target Detection for Truck Driving Environment. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 7: 61-67. https://doi.org/10.25236/AJCIS.2022.050710.

[1] Zhao Lei. Analysis of heavy-duty truck design trends in the context of new transportation[J]. China Aviation Weekly, 2021(36):54-55.

[2] Dong Changqing, Liu Yongxian, Zhao A. Research on vehicle vision detection method based on deep learning algorithm[J]. Manufacturing Automation, 2019, 41(03):119-122.

[3] DOLLAR P,WOJEK C,SCHIELE B,et al. Pedestrian detection:a benchmark [C]. //Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,2009:304-311.

[4] UIJLINGS J R,VAN DE SANDE KE A,GEVERS T,et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013,104(2):154-171.

[5] VEDALD A, GULSHAN V, VARMA M, et al. Multiplekernels for object detection [C]. //IEEE 12th International Conference on Computer Vision,2009:606-613.

[6] YU Y,ZHANG J,HUANG Y,et al. Object detection by context and boosted HOG-LBP[C]. //ECCV Workshop onPASCAL VOC,2010.

[7] Ra M, Jung H G, Suhr J K. Part-based Vehicle Detection in Side-rectilinear Images for Blind-Spot Detection [J]. Expert Systems with Applications, 2018, 101.

[8] Jae Kyu Suhr, Ho Gi Jung. Rearview Camera-Based Backover Warning System Exploiting a Combination of Pose-Specific Pedestrian Recognitions [J]. IEEE Transactions on Intelligent Transportation Systems, 2017, (99): 1-7.

[9] Tang Shi. In-vehicle video-based road vehicle and pedestrian detection [D]. Chengdu: University of Electronic Science and Technology,2018.

[10] Li Jun, Wei Minxiang. Research on large vehicle collision avoidance system based on "fly-eye" sensing network [J]. Agricultural Equipment and Vehicle Program, 2010(3): 3-6.

[11] 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. 2016: 770-778.

[12] Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection [C]. //Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 2117-2125.

[13] Liu S, Qi L, Qin H, et al. Path aggregation network for instance segmentation[C]. //Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 8759-8.