A Novel Lightweight Grape Detection Method
نویسندگان
چکیده
This study proposes a novel lightweight grape detection method. First, the backbone network of our method is Uniformer, which captures long-range dependencies and further improves feature extraction capability. Then, Bi-directional Path Aggregation Network (BiPANet) presented to fuse low-resolution maps with strong semantic information high-resolution detailed information. BiPANet constructed by introducing cross-layer enhancement strategy into Network, fuses more significant reduction in number parameters computational complexity. To improve localization accuracy optimal bounding boxes, Reposition Non-Maximum Suppression (R-NMS) algorithm proposed post-processing. The performs repositioning operations on boxes using position around boxes. Experiments WGISD show that achieves 87.7% mAP, 88.6% precision, 78.3% recall, 83.1% F1 score, 46 FPS. Compared YOLOx, YOLOv4, YOLOv3, Faster R-CNN, SSD, RetinaNet, mAP increased 0.8%, 1.7%, 3.5%, 21.4%, 2.5%, 13.3%, respectively, FPS 2, 8, 26, 0, 10, respectively. Similar conclusions can be obtained another dataset. Encouraging experimental results achieve better performance than other recognized methods tasks.
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ژورنال
عنوان ژورنال: Agriculture
سال: 2022
ISSN: ['2077-0472']
DOI: https://doi.org/10.3390/agriculture12091364