An efficient attention module for instance segmentation network in pest monitoring

Prompt and specialized pest management involving localization and recognition has become a crucial means to prevent pest attacks in modern agriculture. Traditional pest monitoring methods are inaccurate and inefficient due to the hand-crafted features and the low-resolution images. As a result, this study presents an automatic framework that can precisely detect 10 species of pests in the natural environment and assist humans in identifying the locations and contours of pests efficiently. The main contributions of this paper include (1) a novel attention module that encourages the network to focus on the important features; (2) an optimized super resolution approach that is used for both training and testing images to enhance the image quality; (3) a pest monitoring network is proposed by improving the D2Det's structure and adjusting the parameters; and (4) a dataset containing pest images and manually annotated files. Experiments showed that the proposed Pest-D2Det model achieved state-of-the-art performances in terms of the mean Average Precision (mAP) values of detection (78.6%) and segmentation (77.2%). Meanwhile, the performance of our efficient channel and spatial attention network (ECSA-Net) indicated that it is lightweight and effective, which can be integrated into deep learning based models without computation burden.