Abstract:  In order to solve the problem that Yolov3-Tiny algorithm has insufficient feature extraction in gas station monitoring scene detection, which results in low detection accuracy, a new target detection algorithm based on gas station scene is proposed. This method first introduces Mosaic data enhancement algorithm to make the picture contain more feature information. Secondly, InceptionV2 and PSConv ( Poly-Scale Convolution) multiscale feature extraction methods are used to improve the network multiscale prediction ability. Finally, combined with the scSE(Concurrent Spatial and Channel ‘ Squeeze & Excitation’) attention mechanism, the output characteristics of the backbone network are reconstructed. The experimental results show that the algorithm has high detection accuracy and the detection speed meets the actual needs. The performance of the optimized algorithm is greatly improved and can it be applied to other target detection. 

Key words: target detection')">

target detection, you only look once (YOLO) algorithm, feature extraction, attention mechanism, multi-scale prediction