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

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

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)

[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.67] Add attention mechanism MHSA

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

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

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

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

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

yolov3 yolov4 yolov5 yolov6 yolov7 yolov8 yolox Algorithm improvement, algorithm customization, algorithm guidance

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

Target detection algorithm improvement series embedding dynamic snake convolution module DySnakeConv

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 improvements [NO.79] The improved loss function is VariFocal Loss

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.83] Change the backbone feature extraction network Backbone to RevCol

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

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

Yolov5 rotating target detection-remote sensing image detection-UAV rotating target detection-with code and principle

Introduction of Yolov5 small target performance improvement solution

Interpretation and reproduction of target detection paper ten: remote sensing image target detection based on YOLOv5 (code has been reproduced)

Video target detection yolo algorithm small model self-training model comparison (yolov5 yolov7 yolov8)

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

yolov7 improvement series

Yolov8 loss function improvement: Wasserstein Distance Loss, helping small targets to increase points

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.80] introduce plug-and-play CloFormer: the perfect integration of attention mechanism and convolution

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvements [NO.84] are integrated into the 2023 latest large convolution kernel CNN architecture RepLKNet upgraded version-UniRepLKNet

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

Target detection algorithm improvement series embedding Deformable ConvNets v2 (DCNv2)

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