Optimizing Bounding Box Regression by Normalized Intersection over Union with Structured Dual-Center Distance

To mitigate the drawbacks of joint crossover (IoU) in complex detection scenarios, this paper proposes a normalized IoU strategy. This strategy enhances the matching robustness in multi-scale object detection by introducing target scale parameters. The proposed method shows comparable or superior average precision (mAP) performance to traditional methods on public datasets. In addition, we have designed a dual-center distance penalty mechanism that implicitly enforces symmetric constraints between bounding boxes, increasing the number of positive samples detected. Our method has been evaluated on mainstream public datasets and unmanned aerial vehicle (UAV) water level gauge datasets, as well as evaluated using the You Only Look Once (YOLO) framework. Our method increased the average number of positive samples by 2.28% compared to CIoU. It also surpasses the most advanced technology. The dual-center constraint enhances the spatial alignment between bounding boxes. This results in notable performance gains in challenging scenarios. These scenarios involve blurred and heavily occluded objects. After parameter optimization, the proposed method achieves significant accuracy improvements. These improvements are seen in detecting small-scale and occluded characters.