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

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

Enterprise 2023-12-16 20:59:50 views: null

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Origin blog.csdn.net/qq_40716944/article/details/134130153

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

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

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

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

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

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

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

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

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

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

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

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

yolov5 improves spd-conv

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 RCS-OSA module to improve detection speed

yolov5 detects small targets (with source code)

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

Idea advanced debugging skills, make bugs nowhere to hide

Improve YOLOv7 | Add [Triplet] [SpatialGroupEnhance] [NAM] [S2] attention mechanism to the ELAN module | Attached detailed structure diagram

Improve YOLOv7 | Add [CA] [ECA] [CBAM] [SE] attention mechanism in ELAN module and ELAN-H module | Attach detailed structure diagram

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)

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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

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

When YoloV5 Meets Raccoon: Use Yolov5 (v4.0) to make a small raccoon catching apk

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! ! ! ! !

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