Keypoint Detection and Diameter Estimation of Cabbage (Brassica Oleracea L.) Heads Under Varying Occlusion Degrees Via Yolov8n-Ck Network

Author:

Zheng Jinming,Wang Xiaochan,Shi Yinyan,Zhang Xiaolei,Wu Yao,Huang Xuekai,Wang Dezhi,Wang Yanxin,Wang Jihao,Zhang Jianfei

Publisher

Elsevier BV

Reference38 articles.

1. This can be attributed to the phenomenon of repeated detections (i.e., the detection of the same target multiple times), as shown in Fig. 13a. For YOLOv3n-Pose, YOLOv5n-Pose, and YOLOv8n-Pose, the AP 50 and AP 50-95 values perform similarly, ranging from 93.5% to 93.8% and from 92.3% to 93.0%, respectively. The AP 50-95 value of YOLOv8n-CK is the highest, at 6.5%, 6.2%, and 6.9% higher than those of YOLOv3n-Pose, YOLOv5n-Pose, and YOLOv8n-Pose, respectively. This performance can be attributed to the introduction of the CBAM in YOLOv8n-CK, which significantly enhanced the feature extraction capability of the cabbage head detection under complex occlusion conditions;Additionally

2. Development of technology and means for machine harvesting of head cabbage in the non-damaging mode;S Alatyrev;IOP conference series. Earth and environmental science,2020

3. Technology and parameters of cabbage machine harvesting by careful stacking of heads in containers;S S Alatyrev;IOP conference series. Earth and environmental science,2020

4. Image-based size estimation of broccoli heads under varying degrees of occlusion;P M Blok;Biosyst. Eng,2021

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