Improvement of Non-Maximum Suppression in RGB-D Object Detection

Currently, the non-maximum suppression (NMS) algorithm is a commonly used method in the post-processing stage of object detection. However, the NMS algorithm cannot effectively eliminate missing and false object detection results because of the simple constraint condition. To solve the problem of the poor detection effect in highly overlapping dense object scenes in the traditional NMS algorithm, we design an RGB-D object detection network model based on the YOLO v3 framework, and using level-by-level metaphase fusion on the RGB and depth information, we propose an improved NMS algorithm which fuses depth characteristics. According to the depth of the object in the detection boxes, it is determined whether another object is the same object in highly overlapping detection boxes, and the average depth of the internal pixels in the detection boxes is calculated as a penalty term, then the penalty term is added to the detection box score to obtain a new constraint condition for non-maximum suppression. The experimental results on the NYU Depth V2 dataset show that the mean average precision (mAP) of the Depth Fusion NMS algorithm proposed in this paper is 0.8%, 0.5% and 0.3% higher than those of the Greedy-NMS, Soft NMS-L and Soft NMS-G methods, respectively. After comparison and analysis, our method can not only detect more overlapping objects but also achieve a better object localization accuracy.