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

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

Enterprise 2023-10-05 00:50:42 views: null

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Origin blog.csdn.net/m0_51530640/article/details/132303710

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

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 the RepGhostBottleneck module in the C2f module, effectively increasing points

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

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

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

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

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

Improve YOLOv8 | Plug and Play | Add [SE] [CBAM] [ECA] [CA] attention mechanism in the C2F module | Attach a detailed structure diagram

Improve YOLOv8 | Plug and Play | Add [SimAM] [CoTAttention] [SKAttention] [Double Attention] attention mechanism in the C2F module | Attach a detailed structure diagram

Improve YOLOv8 | Feature Fusion | YOLOv8 Application BiFPN Structure | "EfficientDet: Scalable and Efficient Target Detection"

yolov8-pose: Add human body key point detection on yolov8

Yolov8 optimization: Convolution variant --- distribution shift convolution (DSConv), improve the memory efficiency and speed of the convolution layer

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

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

Yolov8 optimization: introduce Soft-NMS to improve the detection accuracy of densely occluded scenes

YOLOv8 latest improvement series: YOLOV8 backbone improvements - Huawei Noah proposes a new backbone architecture VanillaNet. YOLOv8 integrates the power of deep learning minimalism to greatly improve the robustness of the model! !

Practical improvement of YOLOv8 | Replacing the backbone network Backbone with the 2023 latest model LSKNet, rotating target detection SOTA

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.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 strategy: BAM bottleneck attention module | BAM detailed explanation and code comments | CBAM companion article | Effective points

Yolov8 target detection

YoloV8 improvement strategy: NWD new paradigm of small target detection, helping YoloV5 and V8 to violently increase their points on small targets

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

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

Student cheating detection system yolov8

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