TY - JFULL AU - Zixian Zhang and Shanliang Yao and Zile Huang and Zhaodong Wu and Xiaohui Zhu and Yong Yue and Jieming Ma PY - 2024/4/ TI - RV-YOLOX: Object Detection on Inland Waterways Based on Optimized YOLOX through Fusion of Vision and 3+1D Millimeter Wave Radar T2 - International Journal of Electronics and Communication Engineering SP - 55 EP - 62 VL - 18 SN - 1307-6892 UR - https://publications.waset.org/pdf/10013553 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 207, 2024 N2 - Unmanned Surface Vehicles (USVs) hold significant value for their capacity to undertake hazardous and labor-intensive operations over aquatic environments. Object detection tasks are significant in these applications. Nonetheless, the efficacy of USVs in object detection is impeded by several intrinsic challenges, including the intricate dispersal of obstacles, reflections emanating from coastal structures, and the presence of fog over water surfaces, among others. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. The MMW radar is a complementary tool to vision sensors, offering reliable environmental data. This approach involves the conversion of the radar鈥檚 3D point cloud into a 2D radar pseudo-image, thereby standardizing the format for radar and vision data by leveraging a point transformer. Furthermore, this paper proposes the development of a multi-source object detection network, named RV-YOLOX, which leverages radar-vision integration specifically tailored for inland waterway environments. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions. ER -