This study presents a radar-optical fusion detection method for unmanned aerial vehicles (UAVs) in maritime environments. Radar and camera technologies are integrated to improve the detection capabilities of the platforms. The proposed method involves generating regions of interest (ROI) by projecting radar traces onto optical images through matrix transformation and geometric centroid registration. The generated ROI are matched with YOLO detection boxes using the intersection-over-union (IoU) algorithm, enabling radar-optical fusion detection. A modified algorithm, called SPN-YOLOv7-tiny, is developed to address the challenge of detecting small UAV targets that are easily missed in images. In this algorithm, the convolutional layers in the backbone network are replaced with a space-to-depth convolution, and a small object detection layer is added. In addition, the loss function was replaced with a normalized weighted distance loss function. Experimental results demonstrate that compared to the original YOLOv7-tiny method, SPN-YOLOv7-tiny achieves an improved mAP@0.5 (mean average precision at an IoU threshold of 0.5) from 0.852 to 0.93, while maintaining a high frame rate of 135.1 frames per second. Moreover, the proposed radar-optical fusion detection method achieves an accuracy of 96.98%, surpassing the individual detection results of the radar and camera. The proposed method effectively addresses the detection challenges posed by closely spaced overlapping targets on a radar chart.

With the continuous expansion of the application field of artificial intelligence, radar data processing has also begun to fully enter the era of intelligence, and new achievements have emerged in the research fields of target detection, target tracking, and target recognition. At a time of rapid development of artificial intelligence technology, it is necessary to think about the future development of radar data intelligent processing. To this end, combining the research history and the superficial understanding of radar data intelligent processing in the past ten years, our team analyzes the main research progress and challenges of radar data intelligent processing, examines the new requirements for autonomous, multi-modal, and multi-mode radar data intelligent processing, and explores new opportunities in the era of large models, etc. Furthermore, along a research path of ``1 domain foundation model + 3 core task models + N typical application implementations'', the research conceptions and main contents of radar data large model are put forward in order to enable the research of intelligent radar.

Due to the limitations of individual sensors, such as the absence of semantic information in radar and the reduced precision in long-distance ranging for binocular vision, accurate and robust target detection in maritime environments is challenging. To address this issue, this study introduces a novel sensor fusion framework that leverages the complementary strengths of binocular vision and radar. Based on the semantic information provided by RDC-YOLOv7-tiny, a modified stereo matching algorithm is proposed. This algorithm uses a two-dimensional sliding window to effectively correct vertical offsets caused by non-collinearity, while combining the similarity of triangles to reduce computational complexity, achieving more efficient robust matching. A dynamic parallax compensation mechanism is proposed, which employs a standard parallax ratio derived from radar ranging data to continuously calibrate the binocular vision model. This significantly reduces depth estimation errors attributed to installation biases and environmental factors. Ultimately, the corrected high-precision binocular ranging points are spatially and temporally aligned with radar tracking trajectories and fused at the decision level using a probability data association method based on a multi-dimensional cost function. This process yields multi-dimensional target information, including distance, bearing, category, and image evidence. Experimental results demonstrate that the radar-binocular fusion detection method achieves an accuracy rate of 94.7%, significantly surpassing the performance of individual sensors. The mAP@0.5 of the RDC-YOLOv7-tiny algorithm increased from 0.864 to 0.922, while maintaining a frame rate of 138.9 FPS.