YOLO-RDFEA: Object Detection in RD Imagery With Improved YOLOv8 Based on Feature Enhancement and Attention Mechanisms

Abstract

Range-Doppler (RD) imaging has eclipsed synthetic aperture radar (SAR) imaging as the latest hotspot in the field of radar image object detection, owing to its low cost, high speed, and broad application scope. However, RD images are often of low quality due to the loss of effective features. Aiming at the problem of insufficient accuracy of the existing deep-learning-based sea surface RD image object detection, this article presents an improved YOLOv8 object detection algorithm for RD images based on feature enhancement and attention mechanism (YOLO-RDFEA). First, we have designed a feature extraction network DarknetSD with fewer parameters, to provide fine-grained information and compensate for the lack of abstract information. In addition, by introducing the coordinate attention (CA) mechanism in the feature-fusion stage, the model’s attention to spatial and channel features is improved. Moreover, the classification loss is improved using the slide loss function, which enhances the algorithm’s focus on the features of hard examples. Finally, comprehensive tests and evaluations are conducted using a self-built RD image dataset. Compared with the YOLOv8 baseline, the YOLO-RDFEA algorithm significantly reduced the misdetection of ships, its R was elevated by 17.9%. The all-category F1 score increased by 5.1% and mAP0.5 improved by 3.4%, which proved that the algorithm improves the detection and identification performance of all object categories. At the same time, the number of model parameters was reduced by 65.1%, which provides some basis for the algorithm deployment on the hardware platform.

Keywords

• Range-Doppler imagery
• attention mechanism
• small object detection
• remote sensing image
• YOLOv8