关键词: 加油站;  , YOLOv3 模型,  , 感受野模块;  , CSP 网络

Abstract: In view of the safety problems caused by low detection efficiency and illegal operation in traditional gas stations, a modified gas station oil unloading detection method based on YOLOv3 is presented. By introducing RFB (Receptive Field Block ) receptive field module after Darknet-53 backbone output, the model can select appropriate receptive fields to match different scale targets and improve detection accuracy. According to CSP (Cross Stage Partial) network, two RFB_CSP and RFBS_CSP structures are provided to realize cross-level splicing and channel integration and reduce calculation cost. Cluster 9 target reclustering in the field is realized by using K-means++ algorithm to determine appropriate network anchor parameters. The experimental results show that the optimized model contrasts the original YOLOv3 model. The average accuracy is increased by 2. 3% and 2. 9% , indicating that the optimized YOLOv3 model has high practical value in gas station scene detection.

Key words: gas station;  , YOLOv3 model,  , receptive field module,  , cross stage partial (CSP) network