| Summary: | Flies carry pathogens that endanger the health of humans and animals. The color and shape of the fly species are very similar, which is difficult to recognize. This paper proposes a fly species recognition method based on improved RetinaNet and convolutional block attention module (CBAM). Firstly, the proposed method used ResNeXt101 as a feature extraction network, and the improved CBAM called Stochastic-CBAM was added. Then, we built a multi-scale feature pyramid through an improved feature pyramid network (FPN) and integrated multi-level feature information. Finally, the small full convolutional network (FCN) was used as the classification subnet and the bounding box regression subnet. The Kullback-Leibler (KL) loss replaced smooth L1 loss as a bounding box regression loss function for learning bounding box regression and positioning uncertainty at the same time. We experimentally compared the proposed method with other the state-of-the-art methods on the established dataset. Experimental results showed that the mean Average Precision (mAP) of this method reached 90.38%, which was better than the state-of-the-art methods. The average time to recognize a single image was 0.131s. This method has a good detection effect on the fly species recognition.
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