YOLOv8-SAB: Terahertz Image Detection Network Based on Shuffle Attention and YOLOv8

Abstract

The limited accuracy of detecting small objects with current AI algorithm is becoming prominent under background of security check using low-resolved terahertz images. To solve this problem, a modified model based on YOLOv8 architecture – YOLOv8-SAB is built to achieve satisfactory detection of suspicious small objects. Specifically, the model integrates Shuffle attention mechanism into YOLOv8, capturing channel dependence and spatial pixel relationship through Channel Shuffle operation and attention mechanism, thus improving the detection effect of small objects. In addition, BiFPN (Bi-directional Feature Pyramid Network) is used in feature fusion to overcome low resolution of terahertz images, and employ SIoU (SCYLLA IoU) as a friendly optimization object. Experimental results indicate that the inference speed of YOLOv8-SAB reaches 1.7ms to meet the fast detection requirement in real task. YOLOv8-SAB not only shows great potential in small object detection, but also has excellent detection ability for large objects. According to the self-made data set, mAP0.5 and mAP0.5-0.95 for small objects reach 0.93 and 0.716, respectively. As a comparison, mAP0.5 and MAP0.5-0.95 for large objects reach 0.98 and 0.84, respectively. This method plays an important role in the designing and deployment of intelligent real-time terahertz object detection system for security check.

Keywords

• Terahertz image
• small object detection
• deep learning
• YOLO