Research on surface defect detection of photovoltaic floats based on improved RT-DETR

In the process of detecting defects on photovoltaic floats, issues such as missed detection and false alarms are prone to occur due to the complex texture background and varying defect scales. In this study, a method for surface defect detection of photovoltaic floats based on improved RT-DETR is proposed. Firstly, ResNet50 is employed to replace the original backbone network, making the overall model more lightweight. Secondly, the SimAM attention mechanism is introduced to mitigate the influence of complex backgrounds on the detection results, enhancing the model's perception of target defect features. Finally, to address the weak perception of small targets by RT-DETR, the NWD loss function is incorporated. Experimental results show that the proposed algorithm achieves mAP@0.5, F1 score, parameter quantity, and FPS of 95.7%, 94, 40.9, and 25.8, respectively, compared to the original RT-DETR algorithm. This represents an improvement of 1.6% in mAP@0.5, 2.1% in F1 score, and 7.8 in FPS, while reducing the parameter quantity by 26.1m. The results demonstrate that the proposed method meets the requirements for defect detection on photovoltaic floats.