DML-YOLOv8-SAR Image Object Detection Algorithm

Abstract

Given the challenges posed by noise and varying target scales in SAR images, conventional convolutional neural networks often underperform in SAR image detection. To address this, this paper introduces a novel approach. Firstly, a Res-Clo network is proposed for denoising SAR images as a preprocessing step to enhance detection accuracy. Subsequently, an improved network, DML-YOLOv8, is devised based on the YOLOv8 network. The enhancements in the proposed algorithm include several key modifications. Firstly, within the feature extraction layers, a designed MFB module is integrated to effectively broaden the network's receptive field. Next, deformable convolutions are introduced in the feature fusion layers to bolster the network's capability for multi-scale detection. Additionally, a novel loss function, RT-IOU, is designed in feature detection to enhance network inference speed. Finally, a specialized STD small target detection layer is designed to improve detection accuracy for small targets. In practical experiments, it has been shown that the detection method proposed in this paper effectively improves the detection performance of noisy SAR images, and also achieves satisfactory results in multi-scale detection.

CSD-YOLO: A Ship Detection Algorithm Based on a Deformable Large Kernel Attention Mechanism

“…As shown in Figure 3, based on a similar concept, DFL loss models the box position as a general distribution, enabling the network to quickly focus on the vicinity of the label position y and improve the detection accuracy. DFL loss was mainly proposed to address the inflexibility of generated bounding box representations [24]. Traditional object detection struggles to accurately define the true bounding boxes of target objects in complex scenes due to boundary ambiguity and uncertainty caused by factors such as subjective labeling, occlusion, and blurriness.…”