Yiming Ma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yiming Ma has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Dandan Li
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Qin Wang
Chengdu Sinograin Reserves Co., Ltd., Chengdu 610073, China
1 shared publication - Qing Zhou
Sinograin Chengdu Storage Research Institute Co., Ltd., Chengdu 610091, China
1 shared publication - Xiaoping Yan
Sinograin Chengdu Storage Research Institute Co., Ltd, Chengdu, China
1 shared publication - Xueyan Zhu
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Yancheng Zheng
China Reserve Grain Management Group Co., Ltd., Beijing 100044, China
1 shared publication
Latest Publications
- Open Access
A YOLO-Based Model for Detecting Stored-Grain Insects on Surface of Grain Bulks
Accurate, rapid, and intelligent stored-grain insect detection and counting are important for integrated pest management (IPM). Existing stored-grain insect pest detection models are often not suitable for detecting tiny insects on the surface of grain bulks and often require high computing resources and computational memory. Therefore, this study presents a YOLO-SGInsects model based on YOLOv8s for tiny stored-grain insect detection on the surface of grain bulk by adding a tiny object detection layer (TODL), adjusting the neck network with an asymptotic feature pyramid network (AFPN), and incorporating a hybrid attention transformer (HAT) module into the backbone network. The YOLO-SGInsects model was trained and tested using a GrainInsects dataset with images captured from granaries and laboratory. Experiments on the test set of the GrainInsects dataset showed that the YOLO-SGInsects achieved a stored-grain insect pest detection mean average precision (mAP) of 94.2%, with a counting root mean squared error (RMSE) of 0.7913, representing 2.0% and 0.3067 improvement over the YOLOv8s, respectively. Compared to other mainstream approaches, the YOLO-SGInsects model achieves better detection and counting performance and is capable of effectively handling tiny stored-grain insect pest detection in grain bulk surfaces. This study provides a technical basis for detecting and counting common stored-grain insect pests on the surface of grain bulk.
Open AccessA YOLO-Based Model for Detecting Stored-Grain Insects on Surface of Grain Bulks
Accurate, rapid, and intelligent stored-grain insect detection and counting are important for integrated pest management (IPM). Existing stored-grain insect pest detection models are often not suitable for detecting tiny insects on the surface of grain bulks and often require high computing resources and computational memory. Therefore, this study presents a YOLO-SGInsects model based on YOLOv8s for tiny stored-grain insect detection on the surface of grain bulk by adding a tiny object detection layer (TODL), adjusting the neck network with an asymptotic feature pyramid network (AFPN), and incorporating a hybrid attention transformer (HAT) module into the backbone network. The YOLO-SGInsects model was trained and tested using a GrainInsects dataset with images captured from granaries and laboratory. Experiments on the test set of the GrainInsects dataset showed that the YOLO-SGInsects achieved a stored-grain insect pest detection mean average precision (mAP) of 94.2%, with a counting root mean squared error (RMSE) of 0.7913, representing 2.0% and 0.3067 improvement over the YOLOv8s, respectively. Compared to other mainstream approaches, the YOLO-SGInsects model achieves better detection and counting performance and is capable of effectively handling tiny stored-grain insect pest detection in grain bulk surfaces. This study provides a technical basis for detecting and counting common stored-grain insect pests on the surface of grain bulk.