Detecting Pests From Light-Trapping Images Based on Improved YOLOv3 Model and Instance Augmentation.

ABSTRACT: Light traps have been widely used as effective tools to monitor multiple agricultural and forest insect pests simultaneously. However, the current detection methods of pests from light trapping images have several limitations, such as exhibiting extremely imbalanced class distribution, occlusion among multiple pest targets, and inter-species similarity. To address the problems, this study proposes an improved YOLOv3 model in combination with image enhancement to better detect crop pests in real agricultural environments. First, a dataset containing nine common maize pests is constructed after an image augmentation based on image cropping. Then, a linear transformation method is proposed to optimize the anchors generated by the k-means clustering algorithm, which can improve the matching ac

SUBMITTER: Lv J 

PROVIDER: S-EPMC9301456 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature