Improved lightweight YOLOv7-tiny method for road height limitation obstacle detection
In response to the challenges of difficult detection of road height restrictions,complex and bulky models,and difficulties in deploying on embedded devices,a road height obstacle detection method based on an improved lightweight YOLOv7-tiny model is proposed.The improved model utilizes a lighter FasterNet network instead of the original backbone network and incorporates PConv convolution in the Neck layer to reduce computational redundancy and memory access,effectively reducing the model's parameter and computation complexity.The introduction of the CA attention mechanism enhances the detection accuracy,and the Focal-EIoU loss function optimizes the model's convergence speed and efficiency.Experimental results showed that compared to the YOLOv7-tiny object detection model,the improved model achieved a 6.6%increase in mAP@0.5 on the detection dataset,reduced parameters and computations by 24%and 20.5%respectively,and reduced the model weight file by 27.2%.This improved model successfully meets the lightweight requirement while maintaining a high detection accuracy.
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