Weakly Supervised Object Real-time Detection Based on High-resolution Class Activation Mapping Algorithm
Thanks to the development of deep learning technology, object detection techniques have gained wide attention in various vision tasks. However, obtaining bounding box annotations for objects requires high time and labor costs, which hinders the application of object detection technology in practical scenarios. Therefore, a weakly supervised real-time object detection method based on high resolution class activation mapping algorithm is proposed, using only image class labels to reduce the dependence of network on object instance labels. It subdivides object detection into two subtasks: weakly supervised object localization and real-time object detection. In weakly supervised object localization task, a novel High Resolution Class Activation Mapping(HR-CAM) algorithm based on contrastive layer-wise relevance propagation theory is designed. It can obtain high quality class activation maps and generate pseudo detection annotation box. In real-time detection task, Single Shot multibox Detector(SSD) network as object detector is selected and an Object-Aware Loss function(OA-Loss) based on the class activation maps is designed. It can jointly supervise the training process of the SSD network with generated pseudo detection annotation box, to improve the networks' detection performance for objects. The experimental results show that the method proposed in this paper can achieve accurate and efficient object detection on the CUB200 and TJAB52 datasets, verifying the effectiveness and superiority of this method.
万方数据
维普
