关键词: 边缘计算, 目标检测, 小目标, SSD

Abstract:

In order to meet the requirements for safe, accurate and efficient processing of security image data, this paper designs a real-time object detection framework in a low-latency and high-reliability edge computing environment, which is divided into terminal network layer, edge server layer and cloud layer. At the terminal network layer, the BING（Binarized Normed Gradients） algorithm is used to distinguish the object area and background area of the image, and selective compression is applied to different areas to reduce the amount of data transmission. At the edge server layer, an improved SSD（Single Shot Multibox Detector）-based algorithm 2FSSD is proposed to address the problem that small objects in security images are easy to miss detection. The deep level features with high semantic information and low level features in SSD backbone network are fused to form a new feature extraction layer for object detection. Experiments show that the 2FSSD algorithm has a detection accuracy of 79.5% and a detection speed of 27.3 frame/s in VOC data set. Therefore, the research approach can meet the real-time requirements of security image processing while ensuring the detection accuracy.

Key words: edge computing, object detection, small object, SSD