Linjie Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Linjie Wang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Haixia Sun
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
1 shared publication - Liyang Su
College of Science, China Agricultural University, Beijing 100193, China
1 shared publication - Ning Wang
Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078, USA
1 shared publication - Runrun Wang
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
1 shared publication - Shujuan Zhang
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
1 shared publication - Xinyuan Lu
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
1 shared publication
Latest Publications
- Open Access
Cucumber Picking Recognition in Near-Color Background Based on Improved YOLOv5
Rapid and precise detection of cucumbers is a key element in enhancing the capability of intelligent harvesting robots. Problems such as near-color background interference, branch and leaf occlusion of fruits, and target scale diversity in greenhouse environments posed higher requirements for cucumber target detection algorithms. Therefore, a lightweight YOLOv5s-Super model was proposed based on the YOLOv5s model. First, in this study, the bidirectional feature pyramid network (BiFPN) and C3CA module were added to the YOLOv5s-Super model with the goal of capturing cucumber shoulder features of long-distance dependence and dynamically fusing multi-scale features in the near-color background. Second, the Ghost module was added to the YOLOv5s-Super model to speed up the inference time and floating-point computation speed of the model. Finally, this study visualized different feature fusion methods for the BiFPN module; independently designed a C3SimAM module for comparison between parametric and non-parametric attention mechanisms. The results showed that the YOLOv5s-Super model achieves mAP of 87.5%, which was 4.2% higher than the YOLOv7-tiny and 1.9% higher than the YOLOv8s model. The improved model could more accurately and robustly complete the detection of multi-scale features in complex near-color backgrounds while the model met the requirement of being lightweight. These results could provide technical support for the implementation of intelligent cucumber picking.
Open AccessCucumber Picking Recognition in Near-Color Background Based on Improved YOLOv5
Rapid and precise detection of cucumbers is a key element in enhancing the capability of intelligent harvesting robots. Problems such as near-color background interference, branch and leaf occlusion of fruits, and target scale diversity in greenhouse environments posed higher requirements for cucumber target detection algorithms. Therefore, a lightweight YOLOv5s-Super model was proposed based on the YOLOv5s model. First, in this study, the bidirectional feature pyramid network (BiFPN) and C3CA module were added to the YOLOv5s-Super model with the goal of capturing cucumber shoulder features of long-distance dependence and dynamically fusing multi-scale features in the near-color background. Second, the Ghost module was added to the YOLOv5s-Super model to speed up the inference time and floating-point computation speed of the model. Finally, this study visualized different feature fusion methods for the BiFPN module; independently designed a C3SimAM module for comparison between parametric and non-parametric attention mechanisms. The results showed that the YOLOv5s-Super model achieves mAP of 87.5%, which was 4.2% higher than the YOLOv7-tiny and 1.9% higher than the YOLOv8s model. The improved model could more accurately and robustly complete the detection of multi-scale features in complex near-color backgrounds while the model met the requirement of being lightweight. These results could provide technical support for the implementation of intelligent cucumber picking.