In the application of computer vision recognition, the recognition objects of some tissue-like structures present highly complex and variable characteristics. For this practical usage scenario, traditional classification algorithms usually cannot achieve the desired recognition effect. To achieve better recognition performance in this direction, this study integrates ASFF into the YOLOv5 model and introduces the SimAm attention mechanism module. The improved network structure is more lightweight, has fewer network parameters, and the attention mechanism is more effective. We trained the modified YOLO model on datasets, and experimental results show a significant increase in mAP value and a 20% increase in accuracy. This indicates that improvements to the model can significantly enhance performance.
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