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

Enterprise 2023-12-16 20:58:49 views: null

Table of contents

1 RFAConv principle

1.1 Review of standard convolution

1.2 Review of spatial attention

1.4 Innovative spatial attention and standard convolution

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Origin blog.csdn.net/qq_40716944/article/details/134409623

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

Field smoke detection based on Yolov8 (4): Channel Priority Convolution Attention (CPCA), the effect is instantaneous to beat CBAM and SE, etc. | Latest publication of the Chinese Academy of Sciences in 2023

Yolov5/Yolov7 improvement---attention mechanism: ICASSP2023 EMA is based on efficient multi-scale attention of cross-space learning, the effect is better than ECA, CBAM, CA | small target rises significantly

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

yolov5 added CBAM, SE, CA, ECA attention mechanism, pure code (22.3.1 also updated)

Attention mechanism: based on Yolov8's Triplet attention module, plug and play, the effect is better than cbam and se, and the increase point is obvious

Yolov5/Yolov7 improvement --- attention mechanism: Polarized Self-Attention, the effect spikes CBAM, SE

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

Calculation of feature map size, receptive field, and parameter amount after convolution operation

Yolov5/Yolov7 improvement --- attention mechanism: SimAM (no parameter Attention) and NAM (based on standardized attention module), the effect spikes CBAM, SE

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

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

YOLOv5-6.1 adds attention mechanism (SE, CBAM, ECA, CA)

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

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.71] attention mechanism Expectation-Maximization Attention (EMA module)

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.67] Add attention mechanism MHSA

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.85] Use TransformerDecoderHead in RT-DETR to improve yolov5

Yolov8 improvement --- attention mechanism: CoordAttention, the effect spikes CBAM, SE

Yolov8 improves the CoTAttention attention mechanism, and the effect instantly kills CBAM and SE

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

[Selected] Highway Intelligent Inspection System: CBAM Spatial Attention Mechanism Improved YOLOv5

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

Receptive field theory calculations

How to calculate Receptive Field

YOLOv5 improvements | Loss functions | More than 20 loss functions such as EIoU, SIoU, WIoU, DIoU, FocusIoU, etc.

YoloV8 improvement --- attention mechanism: the introduction of GCNet, GENet, the effect is better than SENet

Calculation of Deep CNN Receptive Field

Computer Vision: What is a receptive field?

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