Non-maximum suppression (NMS) as a post-processing step for object detection is mainly used to remove redundant bounding boxes in the object and plays a vital role in many detectors. Its positioning accuracy mainly depends on the bounding box with the highest score, and this strategy is difficult to eliminate the false positive. In order to solve the problem, this paper regards the post-processing step as a combinatorial optimization problem and combines the chaotic whale optimization algorithm and non-maximum suppression. The chaotic search method is used to generate an initial combinatorial solution, and the whale optimization algorithm is discretized to create an updated combinatorial strategy. Under the guidance of the fitness function, the optimal combination is searched. In addition, the method of difference set area (DSA) is proposed to optimize the final detection result. The experiment uses the current mainstream framework Faster R-CNN as the detector on PASCAL VOC2012, COCO2017 and the Warships datasets. The experimental results show that the proposed method can significantly improve the average precision (AP) of detectors compared with the most advanced methods.

非最大抑制（NMS）作为对象检测的后处理步骤，主要用于去除对象中的多余边界框，并且在许多检测器中起着至关重要的作用。其定位精度主要取决于得分最高的边界框，这种策略很难消除误报。为了解决该问题，本文将后处理步骤视为一个组合优化问题，并将混沌鲸鱼优化算法与非最大抑制相结合。混沌搜索方法用于生成初始组合解，而鲸鱼优化算法则离散化以创建更新的组合策略。在适应度函数的指导下，搜索最佳组合。此外，提出了差分集面积法（DSA），以优化最终检测结果。该实验使用当前的主流框架Faster R-CNN作为PASCAL VOC2012，COCO2017和Warships数据集的检测器。实验结果表明，与最先进的方法相比，该方法可以显着提高探测器的平均精度。