Research on target detection algorithm in autonomous driving scenarios based on improved YOLOv5

With the rapid evolution of science and technology, the imperative for autonomous driving has become ever more pressing. In the domain of self-driving technology, the precise and efficient identification of targets within dynamic driving environments remains a paramount challenge. This paper takes on these challenges directly by introducing a pioneering target detection methodology, building upon an enhanced iteration of YOLOv5. Initially, by substituting FasterNet for the original backbone network, the model leverages its partial convolution features to significantly enhance computational efficiency and inference speed. Additionally, the integration of the SimAM attention mechanism dynamically adjusts the significance of various regions within the feature map, empowering the model to prioritize essential image information and refine detection accuracy. To further bolster the model's accuracy in detecting smaller targets, a dedicated module for small target detection is introduced, meticulously extracting feature map details. Lastly, the incorporation of WIoU as the new loss function mitigates the influence of geometric factors, thereby bolstering the model's robustness. Experimental findings demonstrate that the enhanced algorithm elevates mAP and FPS by 2.9% and 2.6 frames per second, respectively, compared to the original model, effectively addressing the challenges of detection speed and accuracy within autonomous driving scenarios.