Deformable convolution and coordinate attention for fast cattle detection

Yang, Wenjie; Wu, Jianlin; Zhang, Jinlai; Gao, Kai; Du, Ruxu; Wu, Ziling; Firkat, Eksan; Li, Dingwen

doi:10.1016/j.compag.2023.108006

Cited by 56 publications

(13 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model was evaluated using five evaluation metrics: precision (P), recall (R), mAP, F1 score and frames per second (FPS) [42]. Eqs.…”

Section: Performance Evaluation Of the Modelsmentioning

confidence: 99%

RSR-YOLO: a real-time method for small target tomato detection based on improved YOLOv8 network

Yue,

Qi,

Yang

et al. 2024

Preprint

View full text Add to dashboard Cite

In tomato producing fields, automated large-area detection method is critical for fruit health monitoring and harvesting. However, due to the limited feature information included in tomatoes, large-area detection across long distances results in more missing or incorrect detections. To address this issue, this research proposes an improved YOLOv8 network, RSR-YOLO, for long-distance identification of tomato fruits. Firstly, this paper designs a partial group convolution (PgConv) and furthermore an innovative FasterNet (IFN) module for feature extraction, taking into account the impact of split operations on the computational complexity of the backbone network. The IFN module is lightweight and efficient, which improves the detection accuracy and real-time detection performance of the model. Secondly, this research combines the Gather and Distribute mechanism (GD) and redesigns the feature fusion module to implement the extraction and fusion of various levels of tomato features, given the critical significance that low-dimensional features play in small target recognition and localization. Finally, Repulsion Loss is used in this paper to examine the impact of fruit overlap and leaf occlusion on detection outcomes. RSR-YOLO achieves precision, recall, F1 score, and mean average precision (mAP@0.5) of 91.6%, 85.9%, 88.7%, and 90.7%, respectively, marking increases of 4.2%, 4%, 4.2%, and 3.6% compared to YOLOv8n. In addition, this paper designs a specialized Graphical User Interface (GUI) for the real-time detection task of tomatoes.

show abstract

“…The model was evaluated using five evaluation metrics: precision (P), recall (R), mAP, F1 score and frames per second (FPS) [42]. Eqs.…”

Section: Performance Evaluation Of the Modelsmentioning

confidence: 99%

RSR-YOLO: a real-time method for small target tomato detection based on improved YOLOv8 network

Yue,

Qi,

Yang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Firstly, we utilize the C2f-DCN module in the backbone feature extraction network to address image distortion caused by pixel compression, resolving challenges in obtaining features for small targets. Secondly, a BiFormer [20] attention module is added to the bottom of the backbone network, focusing on the small fault features of insulators in complex backgrounds and enhancing the network's feature representation capabilities.DCA-YOLOv8 [22] enhances the YOLOv8 architecture by introducing deformable convolutions for capturing finer spatial information and coordinate attention (CA) [23] to emphasize crucial features during the detection process. CSS-YOLO [24] proposes an improved X-ray contraband detection algorithm based on YOLOv8s.…”

Section: Related Workmentioning

confidence: 99%

DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer

Ren,

Song,

et al. 2024

Preprint

View full text Add to dashboard Cite

The detection of lesions in various diseases remains a challenging task in medical image processing, given the diverse morphologies, sizes, and boundaries of lesions associated with different illnesses. In this paper, we propose an advanced lesion detection model named DHC-YOLO, which integrates Multi-Scale Dilated attention (MSDA) and multi-head self-attention (MHSA) within the YOLOv8 network. The method also introduces an enhanced feature fusion through the Concatenation (Concat) operation in the Feature Pyramid Networks (FPN) structure of YOLOv8. The DHC-YOLO model achieves superior performance in lesion detection by effectively aggregating semantic information across various scales in the attended receptive field, reducing redundancy in self-attention mechanisms without the need for complex operations or additional computational costs. The incorporation of MHSA enhances the network’s ability to extract diverse features, and the Concat operation in FPN improves multi-scale feature fusion. Our evaluations on brain tumor, colonic polyp, and esophageal cancer datasets demonstrate the superiority of our method over baseline YOLOv8 and several state-of-the-art object detection models. Specifically, on the brain tumor dataset, DHC-YOLO achieves mAP50 and mAP50:95 scores of 88.3% and 73.5%, respectively; on the colonic polyp dataset, the scores are 88.8% and 67.2%; and on the esophageal cancer dataset, the scores are 51.3% and 20.7%. These compelling results underscore the robust performance of DHC-YOLO in lesion detection tasks.

show abstract

“…YOLOv8n's head is a decoupled head like YOLOX, and it has three output branches. Each output branch is subdivided into a regression branch with a DFL strategy and a prediction branch [18,19]. The model input is augmented with mosaic data, and an anchor-free mechanism is used to directly predict the center of the object, which reduces the number of anchor frame predictions and accelerates the non-maximal suppression.…”

Section: The Yolov8 Networkmentioning

confidence: 99%

Section: The Yolov8 Networkmentioning

confidence: 99%

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

Ding,

Chen,

Gui

et al. 2024

Foods

View full text Add to dashboard Cite

Rose tea is a type of flower tea in China’s reprocessed tea category, which is divided into seven grades, including super flower, primary flower, flower bud, flower heart, yellow flower, scattered flower, and waste flower. Grading rose tea into distinct quality levels is a practice that is essential to boosting their competitive advantage. Manual grading is inefficient. We provide a lightweight model to advance rose tea grading automation. Firstly, four kinds of attention mechanisms were introduced into the backbone and compared. According to the experimental results, the Convolutional Block Attention Module (CBAM) was chosen in the end due to its ultimate capacity to enhance the overall detection performance of the model. Second, the lightweight module C2fGhost was utilized to change the original C2f module in the neck to lighten the network while maintaining detection performance. Finally, we used the SIoU loss in place of the CIoU loss to improve the boundary regression performance of the model. The results showed that the mAP, precision (P), recall (R), FPS, GFLOPs, and Params values of the proposed model were 86.16%, 89.77%, 83.01%, 166.58, 7.978, and 2.746 M, respectively. Compared with the original model, the mAP, P, and R values increased by 0.67%, 0.73%, and 0.64%, the GFLOPs and Params decreased by 0.88 and 0.411 M, respectively, and the speed was comparable. The model proposed in this study also performed better than other advanced detection models. It provides theoretical research and technical support for the intelligent grading of roses.

show abstract

Deformable convolution and coordinate attention for fast cattle detection

Cited by 56 publications

References 18 publications

RSR-YOLO: a real-time method for small target tomato detection based on improved YOLOv8 network

RSR-YOLO: a real-time method for small target tomato detection based on improved YOLOv8 network

DHC-YOLO: Improved YOLOv8 for Lesion Detection in Brain Tumors, Colon Polyps, and Esophageal Cancer

Quality Detection and Grading of Rose Tea Based on a Lightweight Model

Contact Info

Product

Resources

About