关键词: 榆紫叶甲虫, 昆虫识别, 卷积神经网络, Faster R-CNN, 初始候选框调整, K-means聚类算法

Abstract: Aiming at the problem that the traditional image recognition method uses artificially designed feature extraction templates to reduce the recognition accuracy of insects, the deep learning network model Faster R-CNN based on k-means clustering is proposed to identify the targets in the image. Firstly, it uses the k-means clustering algorithm with the BWP index to cluster the aspect ratios of the training data labels, and replaces the aspect ratios of the initial candidate box generated by the standard Faster R-CNN network with the new clustering center point. Then, it improves the size of the initial candidate box. Finally, it sends the training data to the improved Faster R-CNN network for training. The experimental result shows that the Faster R-CNN network with clustering strategy has stronger robustness than the standard Faster R-CNN network when identifying targets with a specific aspect ratio, effectively overcoming the redundancy caused by blade break or holes, the interference of the crustacean of the ambrostoma quadriimpressum motschlsky（aqm）, the mutual influence of the adjacent aqm features and other types of insects with similar characteristics of the aqm. The final recognition accuracy is up to 94.73%. The final recognition accuracy of 94.73% is 4.15% higher than that of the standard network. The method can effectively overcome the limitations of the feature extraction template in traditional insect detection, and has important significance for the identification of insects with such features as delicate and varied attitude.

Key words: ambrostoma quadriimpressum motschlsky, insect recognition, convolutional neural network, Faster R-CNN, initial candidate box adjustment, k-means clustering algorithm