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Aiming at the problem that the existing road vehicle target detection system is prone to misdetection due to excessive computation and complex background, a road vehicle target detection system based on deep learning is designed. The system hardware is mainly composed of four parts: signal input module, control module, central processing module and output channel module. Introduce the XCV50E chip to build a control module, and quickly distribute signals through high-speed RAM. Use TMS320C6202 chip to set up the central processing module, and take PCI9054 as the core device of the signal output module. In the software design, the design of user login, data acquisition and processing, model training, etc. is completed. The deep learning strategy is introduced. First, the histogram equalization method is used to deal with the problem of environmental light interference, then the improved yolov4 model is established to deal with noise and other interference information. Finally, the image feature extraction optimization is completed based on the attention mechanism, and then the road vehicle target detection is completed more accurately. The experimental results show that the proposed system has good robustness, can reduce the amount of calculation and improve the detection accuracy.