Wall segmentation in 2D images using convolutional neural networks by Mihailo Bjekic, Ana Lazovic, Venkatachalam K, Nebojsa Bacanin, Miodrag Zivkovic, Goran Kvascev, Bosko Nikolic

“…An encoder-decoder architecture of the segmentation module was used. Dilated ResNet50/101 network was used as an encoder, representing ResNet50/101 network in which dilated convolutional layers replaced the last convolutional layers. …”
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A population-based study to assess two convolutional neural networks for dental age estimation by Jian Wang, Jiawei Dou, Jiaxuan Han, Guoqiang Li, Jiang Tao

“…Results The VGG16 network outperformed the ResNet101 network in terms of prediction performance. However, the model effect of VGG16 was less favorable than that in other age ranges in the 15–17 age group. …”
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Deep Learning Based Image Recognition Technology for Civil Engineering Applications by Yang Delan

“…In this paper, we use Caffe framework to implement the improved Faster R-CNN recognition technique for building images in civil engineering under Linux system and add feature pyramid network and regional feature aggregation into the ResNet-50 network and ResNet-101 network, respectively, to strengthen the training effect, and establish ResNet-101+FPN+ROI Align image recognition technique. …”
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High-Precision Segmentation of Buildings with Small Sample Sizes Based on Transfer Learning and Multi-Scale Fusion by Xiaobin Xu, Haojie Zhang, Yingying Ran, Zhiying Tan

“…The backbone of the encoder is replaced by the ResNeXt101 network for feature extraction, and the attention mechanism of the skip connection is preserved to fuse the shallow features of the encoding part and the deep features of the decoding part. …”
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Deep-Learning-Based Detection of Transmission Line Insulators by Jian Zhang, Tian Xiao, Minhang Li, Yucai Zhou

“…To improve the recognition accuracy of insulator detection, the MS-COCO pre-training strategy that combines the FPN module with a cascading R-CNN algorithm based on the ResNeXt-101 network is proposed. The purpose of this paper is to systematically and comprehensively analyze mainstream isolator detection algorithms at the current stage and to verify the effectiveness of the improved Cascade R-CNN X101 model by combining the mAP (mean Average Precision) value and other related evaluation indices. …”
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Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis by Sameh Abd El-Ghany, Mohammad Azad, Mohammed Elmogy

“…The experimental findings reveal that the suggested fine-tuned learning model based on the pre-trained ResNet101 network achieves higher results against recent state-of-the-art approaches and other current powerful CNN models.…”
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Improved Faster R-CNN for the Detection Method of Industrial Control Logic Graph Recognition by Shilin Wu, Shilin Wu, Yan Wang, Huayu Yang, Pingfeng Wang

“…The original VGG16 network has been replaced by the ResNet101 network, and the residual value module was introduced to ensure the detailed features of the deep network. …”
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Clothing Image Classification with DenseNet201 Network and Optimized Regularized Random Vector Functional Link by Zhiyu Zhou, Mingxuan Liu, Wenxiong Deng, Yaming Wang, Zefei Zhu

“…We use Accuracy, Macro-F1, Macro-R and Macro-P to assess the algorithm’s ability and compare this algorithm with ResNet50 network, ResNet101 network, DenseNet201 network, InceptionV3 network and different classifiers, which use DenseNet201 as the feature extractor to get the input. …”
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Underwater Object Detection Method Based on Improved Faster RCNN by Hao Wang, Nanfeng Xiao

“…Firstly, we improved the backbone network of the Faster RCNN, replacing the VGG16 (Visual Geometry Group Network 16) structure in the original feature extraction module with the Res2Net101 network to enhance the expressive ability of the receptive field of each network layer. …”
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Defect detection of bearing side face based on sample data augmentation and convolutional neural network by Dan LIANG, Ding Cai WANG, Jia Le CHU, Kai HU, Yong Long XI

“…The ROI align pooling is used to improve the continuity of output features. The Resnet101 network and Leaky Relu activation function are used to avoid the tiny defect feature loss and function dead zone. …”
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Extraction of building from remote sensing imagery base on multi-attention L-CAFSFM and MFFM by Huazhong Jin, Wenjun Fu, Chenhui Nie, Fuxiang Yuan, Xueli Chang

“…Firstly, richer and finer building features are extracted using the ResNeXt101 network and deformable convolution. L-CAFSFM combines feature maps from two adjacent levels and iteratively calculates them from high to low level, and from low to high level, to enhance the model’s feature extraction ability at different scales and levels. …”
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Two-Stage Detection Algorithm for Kiwifruit Leaf Diseases Based on Deep Learning by Jia Yao, Yubo Wang, Ying Xiang, Jia Yang, Yuhang Zhu, Xin Li, Shuangshuang Li, Jie Zhang, Guoshu Gong

“…Based on the mainstream semantic segmentation networks UNet and DeepLabv3+, the experimental results showed that the ResNet101 network achieved the most effective results in the identification of kiwi diseases, with an accuracy rate of 96.6%. …”
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Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision by Wen-Hao Su, Jiajing Zhang, Ce Yang, Rae Page, Tamas Szinyei, Cory D. Hirsch, Brian J. Steffenson

“…The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. …”
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RepVGG-SimAM: An Efficient Bad Image Classification Method Based on RepVGG with Simple Parameter-Free Attention Module by Zengyu Cai, Xinyang Qiao, Jianwei Zhang, Yuan Feng, Xinhua Hu, Nan Jiang

“…The experimental results prove that the classification accuracy of the method proposed in this paper can reach 94.5% for bad images, that the false positive rate of bad images is only 4.3%, and that the inference speed is doubled compared with the ResNet101 network. Our proposed method can effectively identify bad images and provide efficient and powerful support for cyberspace governance.…”
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Classification of Tomato Fruit Using Yolov5 and Convolutional Neural Network Models by Quoc-Hung Phan, Van-Tung Nguyen, Chi-Hsiang Lien, The-Phong Duong, Max Ti-Kuang Hou, Ngoc-Bich Le

“…The ResNet-50, EfficientNet-B0, Yolov5m, and ResNet-101 networks have testing accuracies of 98%, 98%, 97%, and 97%, respectively. …”
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Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN by Xiaoyan Wei, Yirong Wu, Fangmin Dong, Jun Zhang, Shuifa Sun

“…In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. …”
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Modular Neural Networks for Osteoporosis Detection in Mandibular Cone-Beam Computed Tomography Scans by Ivars Namatevs, Arturs Nikulins, Edgars Edelmers, Laura Neimane, Anda Slaidina, Oskars Radzins, Kaspars Sudars

“…The procedure, built using ResNet-101 networks, showcased efficacy in osteoporosis detection using CBCT scans: Stage 1 achieved a remarkable 98.85% training accuracy, Stage 2 minimized L1 loss to a mere 1.02 pixels, and the last stage’s bone thickness computation algorithm reported a mean squared error of 0.8377. …”
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