Three-dimensional target detection algorithm for dangerous goods in CT security inspection

Abstract

In this paper, a 3D dangerous goods detection method based on RetinaNet is proposed. This method uses the bidirectional feature pyramid network structure of RetinaNet to extract multi-scale features from point cloud data and trains the system using Focal Loss function to achieve fast and accurate detection of dangerous goods. In addition, in order to improve the detection accuracy, this paper also introduces the 3D region proposal network (3D RPN) and nonmaximum suppression (NMS) algorithm. The experimental results show that the proposed method performs well on our self-built CT dataset, with high accuracy and low false positive rate, and is suitable for dangerous goods detection tasks in practical scenarios.