Investigations into the behaviour of pedestrians and autonomous driving both frequently employ object detection. It has always been a popular area for research in computer vision and artificial intelligence. Because o...
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Investigations into the behaviour of pedestrians and autonomous driving both frequently employ object detection. It has always been a popular area for research in computer vision and artificial intelligence. Because of the advancement of deep learning, object detectors are improving in accuracy and speed. However, the majority of them struggle to reconcile speed and precision. Because of this, the object detection model employed in this work, which is based on a better You Only Look Once Version 4 (YOLOv4) algorithm, considers both detection accuracy and efficiency. Several different sensor types, including cameras and mmWave (millimetre Wave) radar, are employed to observe the environment. This study employs a YOLOv4-based hybrid feature enhancement network for robust object detection under adverse weather conditions (AWC), utilizing the CRUW dataset. A camera-radar-fused approach that detects things that are cross-supervised by a deep radar object detection network, such as the YOLOv4 algorithm, reduces processing complexity. To correct category imbalance and further boost the robustness of the suggested model, the class-balanced sampling strategy (CBSS) is employed. Python software was employed to analyse the suggested method. In terms of mAP and accuracy, the suggested method is contrasted with the current OD ensemble framework and faster R-CNN methodologies. The suggested method's accuracy and mAP are higher than those of the other existing methods;as a result, the proposed method's mAP produces 40% and its accuracy produces 98.7%, respectively. Additionally, the suggested method performs better when classifying the object and detecting it, but it also strengthens the system's stability and reduces category imbalance.
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