MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. …”

MGVB: a New Proteomics Toolset for Fast and Efficient Data Analysis by Metodi V. Metodiev (6089009)

“…It enables analysis at a fraction of the resources cost typically required by existing commercial and free tools. …”

Example of dataset labeling. by Jinxue Sui (19748080)

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”

Self-built datasets. by Jinxue Sui (19748080)

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”

CSPBottleneck with 2 conversions (C2f) module. by Jinxue Sui (19748080)

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”

YOLOv8 network structure. by Jinxue Sui (19748080)

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”

Decoupling head structure. by Jinxue Sui (19748080)

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”

RE-YOLO network structure. by Jinxue Sui (19748080)

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”

EMA module structure. by Jinxue Sui (19748080)

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”

EMA_C2f network structure. by Jinxue Sui (19748080)

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”

Detailed structure of RFAConv. by Jinxue Sui (19748080)

“…This module makes the spatial semantic features uniformly distributed to each feature group through partial channel reconstruction and feature grouping, which emphasizes the interaction of spatial channels, improves the ability to detect subtle differences, can effectively discriminate the apple occlusion, and reduces the computational cost. Finally, the loss function of YOLOv8 is improved using the Wise Intersection over Union (WIOU) function, which not only simplifies the gradient gain assignment mechanism and improves the ability to detect targets of different sizes, but also accelerates the model optimization. …”

Ablation study visualization results. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

Experimental parameter configuration. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

FLMP-YOLOv8 identification results. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

C2f structure. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

Experimental environment configuration. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

Ablation experiment results table. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

YOLOv8 identification results. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

LSKA module structure diagram. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”

Comparison of mAP curves in ablation experiments. by Xiaozhou Feng (2918222)

“…Finally, the MPDIoU loss function is adopted for bounding box regression, enhancing the precision of localization. …”