Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

“…Additionally, distinct convolutional layers were associated with each fault type: layer 9 for RAF, layer 13 for BRB, layer 16 for RBF, and layer 14 for BBF. …”
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FM-Net: Frequency-Aware Masked-Attention Network for Infrared Small Target Detection by Yongxian Liu, Zaiping Lin, Boyang Li, Ting Liu, Wei An

“…Specifically, we design the wavelet residual block (WRB) to extract multi-scale spatial and frequency features, which introduces a wavelet pyramid as the intermediate layer of the residual block. …”
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A lightweight fabric defect detection with parallel dilated convolution and dual attention mechanism by Zheqing Zhang, Kezhong Lu, Gaoming Yang

“…To increase detection efficiency, a variety of automatic fabric defect detections have been developed. …”
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Defect Detection of Gas Insulation Switch by Infrared Thermography Technology With an Improved Yolo Algorithm by Ma Tianci, Chen Xiangping, Li Bo, Bai Jie

“…The proposed method modifies the YOLOv7 framework by replacing standard CBS modules with Fused-MBConv layers in the backbone to improve feature extraction efficiency while integrates a coordinate attention (CA) mechanism in the neck layer to enhance spatial defect recognition. …”
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Multi-object detection for underground unmanned locomotives based on DYCS-YOLOv8n by XU Jinhui, WANG Wenshan, WANG Shuang, WANG Wenyue, ZHAO Tingting

“…A small-object detection layer was added, increasing the original three layers to four, thereby improving the extraction of fine features and enhancing detection performance for small-sized targets. …”
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MSOAR-YOLOv10: Multi-Scale Occluded Apple Detection for Enhanced Harvest Robotics by Heng Fu, Zhengwei Guo, Qingchun Feng, Feng Xie, Yijing Zuo, Tao Li

“…The accuracy of apple fruit recognition in orchard environments is significantly affected by factors such as occlusion and lighting variations, leading to issues such as missed and false detections. To address these challenges, particularly related to occluded apples, this study proposes an improved apple-detection model, MSOAR-YOLOv10, based on YOLOv10. …”
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A detection algorithm for small surface floating objects based on improved YOLOv5s by Xusheng YUE, Jun LI, Yaohong WANG, Penghao ZHU, Zhexing WANG, Xuanhao XU

“…Method First, data augmentation was performed on the Flow-Img dataset to expand the data and avoid model overfitting. Second, to enhance detection accuracy of the deep learning model for extremely small objects, an additional detection layer was introduced beyond the original three in YOLOv5s, while the detection head for large objects was removed to avoid anchor box allocation issues caused by data imbalance. …”
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Detection of underground personnel safety helmet wearing based on improved YOLOv8n by WANG Qi, XIA Lufei, CHEN Tianming, HAN Hongyin, WANG Liang

“…This paper proposed an improved YOLOv8n model applied to safety helmet wearing detection in underground. A P2 small target detection layer was added to the neck network to enhance the model's ability to detect small targets and better capture details of safety helmets. …”
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SCL-YOLOv11: A Lightweight Object Detection Network for Low-Illumination Environments by Shulong Zhuo, Hao Bai, Lifeng Jiang, Xiaojian Zhou, Xu Duan, Yiqun Ma, Zihan Zhou

“…Meanwhile, the Minimum Point Distance based IoU(MPDIoU) loss function is adopted to mitigate gradient explosion risks while enhancing detection precision. Furthermore, a lightweight detail-enhancement convolution layer and a shared-convolution detection head are designed to improve the model’s capability in capturing fine-grained details. …”
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SCASNet: Spatial Context-Aware Selection Network for Small Object Detection in Aerial Imagery by Zhenkuan Wang, Xue-Mei Dong, Yongli Xu

“…Finally, a content-focused attention module is designed in the detection heads to fuse fine-grained features from the lower layers of the backbone network with semantic features from the neck layers, which enhances the richness of feature representation. …”
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DSR-YOLO: A lightweight and efficient YOLOv8 model for enhanced pedestrian detection by Mustapha Oussouaddi, Omar Bouazizi, Aimad El mourabit, Zine el Abidine Alaoui Ismaili, Yassine Attaoui, Mohamed Chentouf

“…A second version of the C2f block using SimAM and standard convolutions ensures robust feature extraction in deeper layers with optimized computational efficiency. …”
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Enhanced YOLOv5s Model for Improved Multi-Sized Object Detection in Road Scenes by Sangavi Sivanandham, Dharanibai Gunaseelan

“…This combines fine-grained spatial details from the shallow layers with more abstract semantic information from deeper layers, enabling the detection of objects across varying scales. …”
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Deep learning model for early acute lymphoblastic leukemia detection using microscopic images by Vatsala Anand, Prabhnoor Bachhal, Deepika Koundal, Arvind Dhaka

“…Consequently, a deep optimized Convolutional Neural Network (CNN) has been proposed for the early diagnosis and detection of ALL. The design of the deep optimized CNN model consisted of five convolutional blocks with thirteen convolutional layers and five max pool layers. …”
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Research on Face Local Attribute Detection Method Based on Improved SSD Network Structure by Qun Luo, Zhendong Liu

“…On this basis, by organically connecting different layers of the SSD network and integrating convolution block attention module, the improved SSD network structure was used to realize face local attribute detection. …”
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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…In addition, the convolutional layers in the original C3k2 block are optimized using partial convolutions to reduce computational redundancy and improve inference efficiency. …”
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YOLO-SMUG: An Efficient and Lightweight Infrared Object Detection Model for Unmanned Aerial Vehicles by Xinzhe Luo, Xiaogang Zhu

“…The model incorporates an enhanced backbone architecture that integrates the lightweight Shuffle_Block algorithm and the Multi-Scale Dilated Attention (MSDA) mechanism, enabling effective small object feature extraction while significantly reducing parameter size and computational cost without compromising detection accuracy. …”
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Small Target Detection Algorithm for UAV Aerial Images Based on Improved YOLOv7-tiny by ZHANG Guanghua, LI Congfa, LI Gangying, LU Weidang

“…Several ConvMixer layers are also integrated into the end of the backbone network and the detection head. …”
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A Lightweight Conditional Diffusion Segmentation Network Based on Deformable Convolution for Surface Defect Detection by Jiusheng Chen, Yibo Zhao, Haibing Wang

“…Second, the efficient feature extraction block is proposed to address the problem of modeling varying defects, which is designed with a partial deformable convolutional layer that can fully extract geometric features of the diverse defects to further enhance the modeling power of the proposed network. …”
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MSRD-CNN: Multi-Scale Residual Deep CNN for General-Purpose Image Manipulation Detection by Kapil Rana, Gurinder Singh, Puneet Goyal

“…In the literature, several image forensic methods are available to detect specific image processing or editing operations. …”
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Detection of the Pin Defects of Power Transmission Lines Based on Improved TPH-MobileNetv3 by Mengxuan Li, Jingshan Han, Zhi Yang, Bin Zhao, Peng Liu

“…A feature fusion structure with layers of self-attention and a convolutional block attention module (CBAM) is added to the neck network, and a transformer prediction head are added to the head network so that different scale characteristics can be fused and focused from space and channels to strengthen the detection of small targets. …”
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