This study proposes an automated scoring system for cow body condition using improved YOLOv5 to assess the body condition distribution of herd cows,which significantly impacts herd productivity and feeding management....This study proposes an automated scoring system for cow body condition using improved YOLOv5 to assess the body condition distribution of herd cows,which significantly impacts herd productivity and feeding management.A dataset was created by capturing images of the cow's hindquarters using an image sensor at the entrance of the milking hall.This system enhances feature extraction ability by introducing dual path networks and convolutional block attention modules and improves efficiency by replacing some modules from the standard YOLOv5s with deep separable convolution to reduce parameters.Furthermore,the system employs an automatic detection and segmentation algorithm to achieve individual cow segmentation and body condition acquisition in the video.Subsequently,the system computes the body condition distribution of cows in a group state.The experimental findings demonstrate that the proposed model outperforms the original YOLOv5 network with higher accuracy and fewer computations and parameters.The precision,recall,and mean average precision of the model are 94.3%,92.5%,and 91.8%,respectively.The algorithm achieved an overall detection rate of 94.2%for individual cow segmentation and body condition acquisition in the video,with a body condition scoring accuracy of 92.5%among accurately detected cows and an overall body condition scoring accuracy of 87.1%across the 10 video tests.展开更多
The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana ...The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.展开更多
基金supported by the National Key Research and Development Program of China(No.2023YFD1301801)。
文摘This study proposes an automated scoring system for cow body condition using improved YOLOv5 to assess the body condition distribution of herd cows,which significantly impacts herd productivity and feeding management.A dataset was created by capturing images of the cow's hindquarters using an image sensor at the entrance of the milking hall.This system enhances feature extraction ability by introducing dual path networks and convolutional block attention modules and improves efficiency by replacing some modules from the standard YOLOv5s with deep separable convolution to reduce parameters.Furthermore,the system employs an automatic detection and segmentation algorithm to achieve individual cow segmentation and body condition acquisition in the video.Subsequently,the system computes the body condition distribution of cows in a group state.The experimental findings demonstrate that the proposed model outperforms the original YOLOv5 network with higher accuracy and fewer computations and parameters.The precision,recall,and mean average precision of the model are 94.3%,92.5%,and 91.8%,respectively.The algorithm achieved an overall detection rate of 94.2%for individual cow segmentation and body condition acquisition in the video,with a body condition scoring accuracy of 92.5%among accurately detected cows and an overall body condition scoring accuracy of 87.1%across the 10 video tests.
基金supported by the Beijing Science Foundation(No.9232005)the Beijing Municipal Philosophy and Social Science Foundation of China(No.19GLB036)the Beijing Science and Technology Project(No.Z221100005822014)。
文摘The increasing trend towards independent fruit packaging demands a high appearance quality of individually packed fruits.In this paper,we propose an improved YOLOv5-based model,YOLO-Banana,to effectively grade banana appearance quality based on the number of banana defect points.Due to the minor and dense defects on the surface of bananas,existing detection algorithms have poor detection results and high missing rates.To address this,we propose a densitybased spatial clustering of applications with noise(DBSCAN)and K-means fusion clustering method that utilizes refined anchor points to obtain better initial anchor values,thereby enhancing the network’s recognition accuracy.Moreover,the optimized progressive aggregated network(PANet)enables better multi-level feature fusion.Additionally,the non-maximum suppression function is replaced with a weighted non-maximum suppression(weighted NMS)function based on distance intersection over union(DIoU).Experimental results show that the model’s accuracy is improved by 2.3%compared to the original YOLOv5 network model,thereby effectively grading the banana appearance quality.
