GPK-YOLOv5s: Content-Aware Reassembly of Features and Self Attention for High Altitude Parabolic Detection and Tracking

Abstract

High altitude parabolic is difficult to identify because of its small size, fast speed and changeable state, which makes it difficult for subsequent forensics and accountability. This paper proposes a high-altitude parabolic detection and tracking method GPK-YOLOv5s, which integrates Content-Aware Reassembly of Features (CARAFE) and self-attention to realize parabolic detection and tracking. For the detection network, the backbone integrates C3Ghost module to extract effective features and simplify the network. C3Transformer module is embedded in the feature extraction and fusion layer to pay attention to the global context information. The feature fusion layer uses CARAFE module for up sampling to perceive effective features, and integrates shallow features and deep features to form a new large-scale detection layer (Output4) to further obtain smaller receptive fields. Improved multi-scale detection heads are embedded with CBAM to enhance the expression ability of targets. To overcome the frame loss of real-time detection, improved multiscale detection heads are externally connected with Kalman filter to track targets. This experiment verifies that the detection Precision, Recall and F1 value of GPK-YOLOv5s reached 99.0%, 98.6% and 98.8% respectively, which are 2.8%, 4.1% and 3.5% higher than YOLOv5s respectively. And GPK-YOLOv5s is lighter, and the calculation consumption is reduced by 0.4 GFLOPs.