Abstract
Pomelo counting recognition in complex environments faces many difficulties, mainly including changes in illumination, occlusion, and complex backgrounds. These factors contribute to the poor recognition performance of early models such as Faster R-CNN, SSD, and Efficientdet. This study optimized the YOLOv7 object detection network through several strategies for this issue. The main optimization strategies are: (1) Introducing the SEAM module to improve the detection ability for occluded objects; (2) Using the AIFI attention mechanism to enhance the network’s recognition of small objects; (3) Adopting the LAMP pruning technique to reduce model complexity; (4) Utilizing multi-teacher knowledge distillation for model compression and performance enhancement. The results show that under the assisted learning of the student module by the multi-teacher module distillation, the proposed method improves mAP by more than 1.9% compared to the aforementioned early models and other networks. After LAMP pruning, the model size is reduced by at least 93.6%. After knowledge distillation processing, its mAP is increased from 84.2% to 91.1%, achieving high-precision pomelo counting recognition. This study effectively solves automatic pomelo recognition and counting in complex environments. The method can be generalized to other agricultural product detection and statistics scenarios, having broad application prospects.
Published Version
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