Dense-YOLO: A Lightweight Weed Detection Platform Based on MSRCP
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Abstract
For real-time weed detection needs and the flexibility of deploying model in embedded devices. We proposed a lightweight object detection platform, named Dense-YOLO which is based on Multi-scale retinex with chromaticity preservation(MSRCP) and YOLOv4 architecture. First, we use MSRCP to preprocess original images to provide a foundation for subsequent feature extraction. Second, Depthwise separable convolution(DSC) is used to reduce parameters, makes it suitable for developing on embedded devices. Third, we used K-means++ to optimize the clustering of anchor size. Fourth, DenseNet-121, PANet and SPP modules together constitute Dense-YOLO. Last, we analyze the effectiveness of focal loss. Compared with YOLOv4, mAP is improved by 7.26%, three-quarters of the parameters are removed and 6.1 higher in FPS.
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