Does the YOLOv9s model contain the auxiliary branch PGI?Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment Assignees No one assigned Labels None yet Projects None yet Milestone No milestone Development No branches or pull requests 1 part...
Experimental evaluation on the UCPR2019 underwater object dataset shows that the YOLOv9s-UI model has higher accuracy and recall than the existing YOLOv9s model, as well as excellent real-time performance. This model significantly improves the ability of underwater target detection by introducing ...
To solve these problems, the YOLOv9s-SD underwater target detection algorithm is proposed to improve the detection performance in underwater environments. We combine the inverted residual structure of MobileNetV2 with Simple Attention Module (SimAM) and Squeeze-and-Excitation Attention (SE) to form ...
To solve these problems, the YOLOv9s-SD underwater target detection algorithm is proposed to improve the detection performance in underwater environments. We combine the inverted residual structure of MobileNetV2 with Simple Attention Module (SimAM) and Squeeze-and-Excitation Attention (SE) to form ...
To solve these problems, the YOLOv9s-SD underwater target detection algorithm is proposed to improve the detection performance in underwater environments. We combine the inverted residual structure of MobileNetV2 with Simple Attention Module (SimAM) and Squeeze-and-Excitation Attention (SE) to form ...
Input DATASETS indoor-object-detection Language Python License This Notebook has been released under the Apache 2.0 open source license. Continue exploring Input1 file arrow_right_alt Output0 files arrow_right_alt Logs4.0 second run - successful arrow_right_alt Comments0 comments arrow_right_alt...
YOLOv9s; young red pears; object recognition; computer vision; lightweight model1. Introduction With the continuous development of automation and intelligent technologies, automated fruit detection has become a key factor in driving economic growth in the fruit industry and improving agricultural ...
To solve these problems, the YOLOv9s-SD underwater target detection algorithm is proposed to improve the detection performance in underwater environments. We combine the inverted residual structure of MobileNetV2 with Simple Attention Module (SimAM) and Squeeze-and-Excitation Attention (SE) to form ...