Attention-Based Multiscale Feature Pyramid Network for Corn Pest Detection under Wild Environment

SIMPLE SUMMARY: Corn pest recognition and detection is an important step for Integrated Pest Management. Generally, traditional methods adopt manual observation and counting in wild field to monitor the occurrence degree of corn pests. However, this is time-consuming and labor-intensive. An accurate and automatic corn pest detection method based on a deep convolutional neural network has been proposed in this paper. Extensive experimental results on a large-scale corn pest dataset show that the proposed method has good performance and can achieve precise recognition and detection of corn pests. ABSTRACT: A serious outbreak of agricultural pests results in a great loss of corn production. Therefore, accurate and robust corn pest detection is important during the early warning, which can achieve the prevention of the damage caused by corn pests. To obtain an accurate detection of corn pests, a new method based on a convolutional neural network is introduced in this paper. Firstly, a large-scale corn pest dataset has been constructed which includes 7741 corn pest images with 10 classes. Secondly, a deep residual network with deformable convolution has been introduced to obtain the features of the corn pest images. To address the detection task of multi-scale corn pests, an attention-based multi-scale feature pyramid network has been developed. Finally, we combined the proposed modules with a two-stage detector into a single network, which achieves the identification and localization of corn pests in an image. Experimental results on the corn pest dataset demonstrate that the proposed method has good performance compared with other methods. Specifically, the proposed method achieves 70.1% mean Average Precision (mAP) and 74.3% Recall at the speed of 17.0 frames per second (FPS), which balances the accuracy and efficiency.