Multi-Class Object Detection Using Adaptive Non-Maximum Suppression in Dense Images

Deep learning-based object detection technology is actively studied, and non-maximum suppression (NMS) is an algorithm used to remove redundant object detection. NMS creates a bounding box for objects detected using a fixed ratio to determine the probability of an object being present. However, this constraint does not solve the difficulty in detecting objects that significantly overlap or are too small. Soft-NMS is an improvement. Nevertheless, free parameter values are manually chosen to provide non-optimal results. Our proposed Adaptive-NMS algorithm (1) calculates the optimal free parameters in real-time and (2) provides for multi-class object detection in highly dense images, improving results over Soft-NMS up to 12.2%.