Object detection method based on global feature augmentation and adaptive regression in IoT

Abstract

The intelligent processing and utilization of visual perception information is the key technology of Internet of Things (IoT), and object detection based on deep learning is of great significance for improving the intelligence and security of IoT. Due to the influence of factors such as changes in the state of object in actual scene, occlusion and background changes, object detection method of deep learning still has following problems: features extracted by backbone network are noisy and not representative, the positive and negative samples are not balanced, and labeled object is inaccurate due to occlusion. Therefore, this paper proposes an object detection method based on global feature augmentation and adaptive regression. HRFPN extracts more representative high-resolution features and performs global augmentation, which can effectively distinguish feature differences between object and background. In training phase, uniform sampling is introduced to mine hard samples, and the positive and negative samples in RPN phase are balanced to improve detection performance, and adaptive bounding box regression loss is proposed to reduce the influence of object occlusion and boundary blur. Experimental results on PASCAL VOC2007 and MS COCO2017 datasets show that the proposed detection method is superior to the latest methods such as Cascade RCNN, CornerNet and Mask RCNN, which has better robustness and accuracy.