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The cotter pin is a critical component in power transmission lines, as it prevents the loosening or detachment of nuts at essential locations. Therefore, detecting defects in cotter pins is vital for monitoring and diagnosing faults in power transmission systems. Due to environmental factors and human errors, cotter pins are susceptible to loosening and becoming missing. In split pin detection, the primary challenges lie in the small size of the target features and the fine-grained issue of “small inter-class differences and large intra-class variations”. This paper aims to enhance the detection performance of the model for fine-grained small targets by adding a detection head specifically designed for small objects and embedding an attention mechanism. This paper addresses the detection of looseness and missing defects in cotter pins by proposing a target detection model called PMW-YOLOv8 (P-C2f + MCA + WIOU) based on the YOLOv8 framework. The model introduces a specialized small-target detection head (160 × 160), which forms a four-scale pyramid (P2–P5) through cross-layer aggregation, effectively utilizing shallow features. Additionally, it incorporates a multidimensional collaborative attention (MCA) module to enhance the features transmitted to the detection head. To further address the fine-grained feature extraction problem, a polarization self-attention mechanism is integrated into C2f, leading to the proposed P-C2f module. Finally, the WIOU loss function is applied to the model to mitigate the impact of sample quality fluctuations on training. Experiments were conducted on a cotter pin defect dataset to validate the model’s effectiveness, achieving a detection accuracy of 66.3%, an improvement of 3% over YOLOv8. The experimental results demonstrate that our model exhibits strong robustness and generalization, enabling it to extract more profound and comprehensive features. Full article