A LIGHTWEIGHT MILLET DOWNY MILDEW SPORE DETECTION METHOD BASED ON IMPROVED YOLOv8s

This paper proposes a lightweight spore detection method for millet downy mildew based on an improved YOLOv8s, aiming to enhance the accuracy and efficiency of spore detection. First, the backbone network of the YOLOv8s model was modified by replacing the original backbone with EfficientViT. The substitution of the EfficientViT backbone enables global receptive field and multi-scale learning, which helps to reduce computational costs. While maintaining high performance, this modification significantly improves computational efficiency. Second, a Frequency-Adaptive Dilated Convolution (FADC) module was added to the neck of the model. By adaptively adjusting the receptive field of dilated convolution, the FADC module optimizes the detection of different frequency information. It improves the detection of small objects without adding extra computational burden. Finally, the detection head was optimized to better adapt to the task of detecting millet downy mildew spores, resulting in enhanced detection speed and accuracy. The improved algorithm, named EFP-YOLOv8s, maintains the same mAP50 as the original YOLOv8s model while reducing the number of parameters by 37.8% and computational cost by 58.5%. By balancing high performance with reduced computational resource demands, the model achieves lightweight design, making it more deployable and scalable in practical applications.