Corner‐based object detection method for reactivating box constraints

Corner‐based detectors usually generate a large number of false detection boxes because of insufficient attention to the detection area. Recent corner‐based detectors can achieve good performance, but the training equipment requirements have greatly increased. For example, due to the dense network structure and the large input image size, CenterNet requires expensive equipment for network training (e.g. Tesla V100). Its performance will be greatly reduced when a more mainstream and cheaper device is used for fine‐tuning. The high equipment requirements make it difficult for most researchers to follow up these studies. In this work, CenternessNet, a detector that adds additional box‐edge length constraints to CenterNet is proposed, thereby allowing the network to be trained on more general devices and obtain a better performance. It simply introduces the box as a constraint into the corner‐based network. In this way, the method improves the ability to aggregate corners during training and enhances the model's ability to discriminate the corners of objects in the same category to some extent. The method achieves a better performance than other corner‐based detection networks trained on similar low‐memory devices.