Abstract: We present an improved target detection algorithm based on Yolov3-Tiny for gas station scene because of the low accuracy of target detection algorithm in gas station scenes. This algorithm takes Yolov3-Tiny model as the basic network, innovates Mosaic image enhancement method proposed in Yolov4 algorithm for data preprocessing, uses dense connection modules to reconstruct the feature extraction network, and adds CBAM (Convolutional Block Attention Module) attention mechanism and Pyramid Pooling Module into the network, finally target detection in the gas station scene is realized. The experimental results show that the improved algorithm improves the overall mAP by 8. 2% compared with the original algorithm, and can be more effectively applied to gas station target detection.

Key words: target detection, dense connection module, attention mechanism, pyramid pool module, image enhancement