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

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

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Origin blog.csdn.net/m0_70388905/article/details/128720459

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

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

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

CFPNet based on YoloV5---ECVBlock's small target detection, plug and play, helping to detect rising points

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

YoloV8 rising point artifact: CFPNet-ECVBlock's small target detection, plug and play, helping to detect rising points

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

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

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

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

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

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

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

[YOLOv7/YOLOv5 series algorithm improvement NO.7] Loss function improvement

Gain point artifact: small target performance improvement based on Yolov5/Yolov7

yolov7 improvement series

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

[YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement NO.65] CVPR 2023 | Tsinghua team's plug-and-play network architecture—Slide-Transformer

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.70] NonDeepNe (ParNet module), a lightweight attention mechanism that does not increase network depth

One article to improve your programming ability, Python ability improvement series-modular programming

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

[YOLO series] YOLOv5, YOLOX, YOOv6, YOLOv7 network model structure

[Amélioration de la série YOLOv7/YOLOv5 NO.53] Intégration du module ECVBlock dans le réseau CFPNet pour améliorer la capacité de détection des petites cibles

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 loss function improvement: Wasserstein Distance Loss, helping small targets to increase points

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

[YOLOv7/YOLOv5 series algorithm improvement NO.8] Non-maximum suppression NMS algorithm improvement Soft-nms

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