YOLOV5 improvement: Add RCS-OSA module to improve detection speed - Code World

YOLOV5 improvement: Add RCS-OSA module to improve detection speed

Enterprise 2023-10-05 00:50:37 views: null

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Origin blog.csdn.net/m0_51530640/article/details/132304378

YOLOV5 improvement: Add RCS-OSA module to improve detection speed

YOLOV7 improvement: Add RCS-OSA module to improve detection speed

YOLOV8 improvement: Add RCS-OSA module to improve detection speed

YOLOV8 improvement: Add RCS-OSA module to improve detection speed

YOLOv5 improvement: Introduce DenseNet idea to create dense connection module, completely improve target detection performance

[YOLOv7/YOLOv5 series improvement NO.53] Integrating the ECVBlock module in the CFPNet network to improve the detection ability of small targets

Optimize and improve the YOLOv5 algorithm by adding the DCNv3 module to effectively improve the target detection effect

Optimize and improve the YOLOv5 algorithm: add the SPD-Conv module to make small targets have nowhere to hide - (super detailed)

Practical improvement of YoloV5: using MPDIoU to improve YoloV5

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.69] Improvement of small targets in remote sensing image target detection CATnet (ContextAggregation module)

YOLOV5 improvement: Add D-LKA Attention at different locations in the C3 module (with the ability to have both SA attention and large convolution kernels)

YOLOV8 improvement: add the RepGhostBottleneck module in the C2f module, effectively increasing points

[Target detection] YOLOv5: Add missed detection rate and false detection rate output

YOLOv8’s latest improvement series: Optimize CIoU to Wise-IoU to help improve detection performance and effectively improve small target detection accuracy! ! ! Far ahead! ! ! ! !

YOLOv5 improvement series (11) - add loss function EIoU, AlphaIoU, SIoU, WIoU

[YOLOv7/YOLOv5 series improvement NO.52] Integrating the C2f module in YOLOv8

23 years of yolov5 vehicle recognition + pedestrian recognition + license plate recognition + speed detection code (python)

yolov5 deepsort pedestrian/vehicle (detection + counting + tracking + ranging + speed measurement)

Improving YOLOv5/YOLOv8: Reproduction combined with plug-and-play | Efficient multi-scale attention (EMA), the module becomes a small helper for YOLOv5 improvement

Optimize and improve YOLOv5 algorithm for small target detection optimization YOLOv5_Face2 (super detailed)

Yolov5/Yolov7 optimization: introduce Soft-NMS to improve the detection accuracy of densely occluded scenes

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.67] Add attention mechanism MHSA

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.85] Use TransformerDecoderHead in RT-DETR to improve yolov5

YOLOV5 improvement: CVPR 2023 | Add EMA attention mechanism in different positions of C3 block, effectively increase points

YOLOv5 Improvement Series (20) - Add BiFormer Attention Mechanism (CVPR2023|Small Goal Increase Point Artifact)

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.71] attention mechanism Expectation-Maximization Attention (EMA module)

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.72] attention mechanism Linear Context Transform Block (LCT module)

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.73] Adding asymptotic feature pyramid network (AFPN module)

YOLOV8 improvement: CVPR 2023 | Add EMA attention mechanism in different positions of C2f module, effectively increase points

yolov5 target detection

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