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Object detection is a core component of perception systems, providing the egovehicle with information about its surroundings to ensure safe route planning.While cameras and Lidar have significantly advanced perception systems, theirperformance can be limited in adverse weather conditions. In contrast,millimeter-wave technology enables radars to function effectively in suchconditions. However, relying solely on radar for building a perception systemdoesn't fully capture the environment due to the data's sparse nature. Toaddress this, sensor fusion strategies have been introduced. We propose adual-branch framework to integrate radar and Lidar data for enhanced objectdetection. The primary branch focuses on extracting radar features, while theauxiliary branch extracts Lidar features. These are then combined usingadditive attention. Subsequently, the integrated features are processed througha novel Parallel Forked Structure (PFS) to manage scale variations. A regionproposal head is then utilized for object detection. We evaluated theeffectiveness of our proposed method on the Radiate dataset using COCO metrics.The results show that it surpasses state-of-the-art methods by $1.89\%$ and$2.61\%$ in favorable and adverse weather conditions, respectively. Thisunderscores the value of radar-Lidar fusion in achieving precise objectdetection and localization, especially in challenging weather conditions.

Radar-Lidar Fusion for Object Detection by Designing Effective Convolution Networks