Abstract
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.
Highlights
With the continuous development of international trade, the types and quantities of inbound goods have increased significantly, which may lead to the invasion of alien species
The experimental results show that our proposed method have better performance compared with the state-of-the-art methods for fly species recognition
In this paper, we propose a fly recognition method based on improved RetinaNet and convolutional block attention module (CBAM), which accurately locates and recognizes flies
Summary
With the continuous development of international trade, the types and quantities of inbound goods have increased significantly, which may lead to the invasion of alien species. Wang et al [4] designed an automatic recognition system for insect specimen images, using artificial neural networks and support vector machines to train and learn image features These methods need to manually select feature. Zhao et al proposed M2Det [19], which applies a multi-level feature pyramid network to construct effective feature pyramids for detecting objects of different scales This method has good detection accuracy and detection speed, but the accuracy is low when recognizing small objects. For the purpose of improving the accuracy of similar object recognition, we improve FPN network, introduce a bottom-up path augmentation method, and add the improved CBAM [23] to the feature extraction network of the RetinaNet model.
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