Yolov5创新:NEU-DET钢材表面缺陷检测,优化组合新颖程度较高,CVPR2023 DCNV3和InceptionNeXt,涨点明显 1.钢铁缺陷数据集介绍 NEU-DET钢材表面缺陷共有六大类,分别为:'crazing','inclusion','patches','pitted_surface','rolled-in_scale','scratches' 2.基于yolov5s的训练 map值0.742: 2.1 Inception-MetaNeXtStag...
(bidirectional feature pyramid network,BiFPN)增强图像高层语义信息和低层特征信息融合性能,在输出端引入SPD-Conv提高模型对低分辨率物体的检测能力;最后,提出SimCS-CA模块并引入特征融合网络增强模型的特征表示性能.实验结果表明,PC-YOLOv7算法在NEU-DET数据集上平均精度均值(mAP)达到了78.5%,相比原始YOLOv7-tiny算法...
The experimental results show that on the Northeastern University Defect Detection (NEU-DET) dataset, the improved YOLOv7 network model improves the mean average precision (mAP) by 6% compared to that of the original algorithm. Zhao et al.33 developed the stem module and combined it with the ...
We performed extensive experiments on the publicly available dataset NEU-DET to verify the effectiveness of the modified YOLOv7-tiny model. Compared with the current advanced target detection models such as Faster-RCNN, YOLOv5, YOLOX, and YOLOv7, the improved YOLOV7-tiny model significantly ...
Experimental results demonstrate that the proposed model achieves an mAP of 0.771 on the NEU-DET dataset, representing a 3.6% improvement over the original model. It outperforms some state-of-the-art detectors and meets the real-time industrial detection requirements.Weifeng Zhang...
Additionally, the detection head in the head section is replaced with an Efficient Decoupled Detection Head, enhancing the model's capability to classify and locate small defects. The proposed model is tested on the public dataset NEU-DET, achieving a high mAP of 76,5 %. This effectively ...
The algorithm was evaluated using the NEU-DET dataset and actual data collected from metal pistons. To evaluate the performance of the propose DA-YOLOv7, we introduce a new metal piston dataset. The results show that it has impressive performance in detecting defects in metal pistons. ...
Finally, we verified the model through the NEU-DET data set. The experimental results show that, with these improvements, the mAP@0.5 of the model is significantly improved, reaching 0.768 in the end, and the reasoning speed is also up to 34.5 fps, which proves the speed and accuracy of ...
It achieves detection accuracies of 78.3%, 87.3%, and 83.5% on three industrial component datasets (NEU-DET, TCAP-DET, and GC10-DET), respectively. Compared to YOLOv7, it significantly improves detection frame rates by 4.9% (an increase of 11.3 FPS), while achieving a better performance ...
The experimental results on the NEU-DET dataset show that SS-YOLO achieves a 97% mAP50 accuracy, which is a 4.5% improvement over that of YOLOv7. Additionally, there was a 79.3% reduction in FLOPs(G) and a 20.7% decrease in params. Thus, SS-YOLO demonstrates an effective balance ...