CCDS-YOLO: Multi-Category Synthetic Aperture Radar Image Object Detection Model Based on YOLOv5s

Abstract

Synthetic Aperture Radar (SAR) is an active microwave sensor that has attracted widespread attention due to its ability to observe the ground around the clock. Research on multi-scale and multi-category target detection methods holds great significance in the fields of maritime resource management and wartime reconnaissance. However, complex scenes often influence SAR object detection, and the diversity of target scales also brings challenges to research. This paper proposes a multi-category SAR image object detection model, CCDS-YOLO, based on YOLOv5s, to address these issues. Embedding the Convolutional Block Attention Module (CBAM) in the feature extraction part of the backbone network enables the model’s ability to extract and fuse spatial information and channel information. The 1 × 1 convolution in the feature pyramid network and the first layer convolution of the detection head are replaced with the expanded convolution, Coordinate Conventional (CoordConv), forming a CRD-FPN module. This module more accurately perceives the spatial details of the feature map, enhancing the model’s ability to handle regression tasks compared to traditional convolution. In the detector segment, a decoupled head is utilized for feature extraction, offering optimal and effective feature information for the classification and regression branches separately. The traditional Non-Maximum Suppression (NMS) is substituted with the Soft Non-Maximum Suppression (Soft-NMS), successfully reducing the model’s duplicate detection rate for compact objects. Based on the experimental findings, the approach presented in this paper demonstrates excellent results in multi-category target recognition for SAR images. Empirical comparisons are conducted on the filtered MSAR dataset. Compared with YOLOv5s, the performance of CCDS-YOLO has been significantly improved. The mAP@0.5 value increases by 3.3% to 92.3%, the precision increases by 3.4%, and the mAP@0.5:0.95 increases by 6.7%. Furthermore, in comparison with other mainstream detection models, CCDS-YOLO stands out in overall performance and anti-interference ability.

Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images

“…The experimental results showed that, compared to traditional data augmentation methods, the proposed method achieved significant improvements in detection accuracy and reduced missed detection rates. Huang et al [26] proposed a CCDS-YOLO multi-category synthetic aperture radar (SAR) image target detection model. To evaluate model performance, the researchers conducted experiments on the filtered MSAR dataset, using comprehensive evaluation metrics including precision, recall, average precision (AP), mean average precision (mAP), etc.…”

Comparative Study of Sentinel-1-Focused and Simulated SAR Images Using LiDAR Point Cloud Modeling for Coastal Areas

“…Synthetic Aperture Radar (SAR) imaging techniques have been increasingly extensively applied in various fields [1][2][3][4][5], such as military reconnaissance, ocean remote sensing, meteorological observation, and various other domains, due to its day-night and all-weather monitoring capability [6][7][8][9] Among these, earth observation is one of the most significant applications of SAR [10,11], enabling mapping, geological exploration, environmental monitoring, and resource survey [12]. All of these applications leverage SAR data [13] and rely strongly on advanced imaging algorithms for efficient and accurate imaging.…”

Target Localization and Grasping of NAO Robot Based on YOLOv8 Network and Monocular Ranging

“…However, the detection accuracy of this method is relatively low since target detection in the robot's image is complicated by a complex background and target occlusion. Huang M. et al proposed a multi-category SAR image object detection model based on YOLOv5s to address the issues caused by complex scenes [3]. Tan L.et al adopted the hollow convolution to resample the feature image to improve the feature extraction and target detection performance [4].…”