Novel Defect Detection for a Badminton Shuttlecock Based on Improved YOLOv8 with RepVGGBlock

In badminton shuttlecock secondary recycling, manual selection is easily affected by human factors such as fatigue and attention lapses. This leads to low efficiency, making it impossible to meet large-scale refurbishment needs. This paper proposes YOLOv8n_RepVGG, an enhanced badminton shuttlecock classification method from a modified YOLOv8n architecture. We use RepVGGBlock modules in the backbone network to improve the model's representational capacity. Compared to baseline YOLOv8, the proposed model achieves a precision of 91.1 with an increase of 3.1 and a mean average precision mAP50 of 87.2 with an increase of 1.2 . The proposed approach not only propels technological advances in badminton shuttlecock reconditioning processes but also contributes significantly to global sustainability initiatives through enhanced resource optimization.