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

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

News 2023-09-18 21:16:55 views: null

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Origin blog.csdn.net/m0_63774211/article/details/132984011

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

NEU-DET steel surface defect detection based on Yolov8, the optimized combination is highly novel: CVPR2023 PConv and BiLevelRoutingAttention, with obvious growth points

Artifact with rising points: CVPR2023 InceptionNeXt. When Inception meets ConvNeXt, plug and play in Yolov5/Yolov7/Yolov8, small target detection has obvious points.

Safety helmet detection system based on YOLOv8 (3): DCNv3 deformable convolution, optimized based on DCNv2, to assist behavior detection | CVPR2023 InternImage

Interpretation and reproduction of SCI paper [NO.3] MSFT-YOLO: Improved YOLOv5 steel surface defect detection based on transformer (code has been reproduced)

Thesis research | Steel surface defect detection based on machine vision

YOLOX&NEU-DET steel surface defect task division data set

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

See "Machine Vision Surface Defect Detection Technology and Its Application in Iron and Steel Industry" notes

PCB defect detection based on yolov5

Photovoltaic cell defect detection based on Yolov8 introduces ICCV2023 dynamic snake convolution and the exclusive network-first multi-dimensional collaborative attention module MCA, achieving full innovation and growth

Artifact with rising points: CVPR2023 InceptionNeXt. When Inception meets ConvNeXt, plug and play in Yolov5/Yolov7/Yolov8, small target detection has obvious points.

5 Key Points to Improve Software Defect Detection Rate

Algorithm yolov5 for identification and detection of rubbish on river surface

2023 Certification Cup Mathematical Modeling Question B Industrial Surface Defect Detection Ideas + Code

Yolov8 actual combat: traffic signal roadsign recognition, by adding CVPR203 DCNV3 and BiLevelRoutingAttention, violent increase

Pytorch-YOLOv3-DAGM2007 defect detection

Rail defect/crack detection based on yolov3

python-opencv surface defect detection (pattern recognition)

YOLOV5 improvement: CVPR 2023 | Add EMA attention mechanism in different positions of C3 block, effectively increase points

Overcoming the key technology of high-gloss surface defect detection, PaintPro paint surface defect detection system is newly released

[Source code and deployment tutorial] PCB board defect detection system: improved YOLOv5 & OpenCV

Surface defect detection of new energy vehicle lithium battery based on high-precision 3D machine vision

Intelligent defect detection system based on AI image recognition, application in the steel industry - technical solution

[Interpretation and reproduction of target detection paper NO.33] Improved YOLOv5 new energy battery collector plate defect detection method

To help industrial intelligent production quality inspection, develop and build a hot-rolled steel defect detection and recognition system based on yolov5n/s/m series models with different precisions, and compare and analyze performance differences

CVPR2023 Target Detection Paper Collection

PCB defect detection and counting system based on YOLOv5+Deepsort

Convert yolov5 target detection labels based on semantic segmentation Ground Truth (GT) (example of road surface water detection)

Comparative Study of Different Image Enhancement Methods for Detection and Classification of Steel Surface Defects

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