Abstract
Insect pests strongly affect crop growth and value globally. Fast and precise pest detection and counting are crucial measures in the management and mitigation of pest infestations. In this area, deep learning technologies have come to represent the method with the most potential. However, for small-sized crop pests, recent deep-learning-based detection attempts have not accomplished accurate recognition and detection due to the challenges posed by feature extraction and positive and negative sample selection. Therefore, to overcome these limitations, we first designed a co-ordinate-attention-based feature pyramid network, termed CAFPN, to extract the salient visual features that distinguish small insects from each other. Subsequently, in the network training stage, a dynamic sample selection strategy using positive and negative weight functions, which considers both high classification scores and precise localization, was introduced. Finally, several experiments were conducted on our constructed large-scale crop pest datasets, the AgriPest 21 dataset and the IP102 dateset, achieving accuracy scores of 77.2% and 29.8% for mAP (mean average precision), demonstrating promising detection results when compared to other detectors.