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

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

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Origin blog.csdn.net/qq_40716944/article/details/134236647

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

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

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

Optimize and improve the receptive field attention convolution operation (RFAConv) of the YOLOv5 algorithm, and the effect is faster than CBAM and CA, etc.

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

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

Analysis of YOLOv5 algorithm for target detection

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

How to improve the performance of small target detection

Analysis of Yolov5 target detection algorithm: model structure

Analysis of YOLOv3 algorithm for target detection

yolov5 target detection

[Target Detection] Theory (3) YOLOv5 implementation

Optimize Java code efficiency and algorithm design to improve performance

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

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

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

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

Adding Res2Net module to Yolov5 improved algorithm

Yolov5 innovation: NEU-DET steel surface defect detection, the optimized combination is highly novel, CVPR2023 DCNV3 and InceptionNeXt have obvious growth points

[Target detection algorithm] IOU, GIOU, DIOU, CIOU and YOLOv5 loss function

What is the role of normalizing the input layer data in the YOLOv5 target detection algorithm?

New Inner-IoU loss function! ! ! Effectively improve detection results by calculating IoU through auxiliary bounding boxes

Target detection algorithm (R-CNN, fast R-CNN, faster R-CNN, yolo, SSD, yoloV2, yoloV3, yoloV4, yoloV5, yoloV6, yoloV7)

[Target detection] YOLOV5 detailed explanation

Installation of target detection yolov5

The basic idea of Yolov5 target detection

YOLOV5 target detection record

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