YOLO-Based Light-Weight Deep Learning Models for Insect Detection System with Field Adaption

Abstract

The most incredible diversity, abundance, spread, and adaptability in biology are found in insects. The foundation of insect study and pest management is insect recognition. However, most of the current insect recognition research depends on a small number of insect taxonomic experts. We can use computers to differentiate insects accurately instead of professionals because of the quick advancement of computer technology. The “YOLOv5” model, with five different state of the art object detection techniques, has been used in this insect recognition and classification investigation to identify insects with the subtle differences between subcategories. To enhance the critical information in the feature map and weaken the supporting information, both channel and spatial attention modules are introduced, improving the network’s capacity for recognition. The experimental findings show that the F1 score approaches 0.90, and the mAP value reaches 93% through learning on the self-made pest dataset. The F1 score increased by 0.02, and the map increased by 1% as compared to other YOLOv5 models, demonstrating the success of the upgraded YOLOv5-based insect detection system.