Fuzzy deep learning architecture for cucumber plant disease detection and classification by Anas Bilal, Junaid Ali Khan, Abdulkareem Alzahrani, Khalid Almohammadi, Maha Alamri, Xiaowen Liu

“…The proposed architecture incorporates 40 convolutional layers, 4 pooling layers, 4 inverted bottleneck blocks, 4 bottleneck blocks, 5 fuzzy layers, and a fully connected layer designed to enhance accuracy and stability when analyzing remotely sensed data. …”
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Barefoot Footprint Detection Algorithm Based on YOLOv8-StarNet by Yujie Shen, Xuemei Jiang, Yabin Zhao, Wenxin Xie

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PA-YOLO-Based Multifault Defect Detection Algorithm for PV Panels by Wang Yin, Zhao Jingyong, Xie Gang, Zhao Zhicheng, Hu Xiao

“…For the occlusion problem of dense targets in the dataset, we introduce a repulsive loss function, which successfully reduces the occurrence of false detection situations. Finally, we propose a customized convolutional block equipped with an EMA mechanism to enhance the perceptual and expressive capabilities of the model. …”
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DGA domain detection and botnet prevention using Q-learning for POMDP by Y. V. Bubnov, N. N. Ivanov

“…The described method implies the detection of generated domain names in DNS queries using a neural network with parallel organization of convolutional and bidirectional recurrent layers. …”
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Multi-defect detection and classification for aluminum alloys with enhanced YOLOv8. by Ying Han, Xingkun Li, Gongxiang Cui, Jie Song, Fengyu Zhou, Yugang Wang

“…However, state-of-the-art material defect detection methods have low detection accuracy and inaccurate defect target frame problems. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…These feature maps capture both high-level semantic information and low-level spatial details, which are essential for detecting pedestrians in complex scenes. To enhance the feature representation and reduce background noise interference, the Convolutional Block Attention Module (CBAM) is embedded after the feature maps. …”
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Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC by Chenxi Liang, Yang Zhao, Fei Kang

“…Due to the scarcity of existing images of underwater concrete defects, this study establishes a dataset of underwater defect images by manually constructing defective concrete walls for the training of defect detection networks. For the defect feature ambiguity that exists in underwater defects, the ConvNeXt Block module and Efficient-RepGFPN structure are introduced to enhance the feature extraction capability of the network, and the P2 detection layer is fused to enhance the detection capability of small-size defects such as cracks. …”
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Multi-Scale Construction Site Fire Detection Algorithm with Integrated Attention Mechanism by Haipeng Sun, Tao Yao

“…First, considering the wide range of scale variations in detected objects, an additional detection layer with a 64-times down-sampling rate is introduced to enhance the algorithm’s detection capability for multi-scale targets. …”
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Small target detection in coal mine underground based on improved RTDETR algorithm by Feng Tian, Cong Song, Xiaopei Liu

“…In order to increase the accuracy of tiny object detection and concentrate on the detail information in the shallow feature map, the small object detection layer P2 is simultaneously added to the Head of the coding section. …”
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Multi-granularity representation learning with vision Mamba for infrared small target detection by Yongji Li, Luping Wang, Shichao Chen

“…Specifically, we tailor a nested structure with cross-fertilization of global and local information. Each layer of the top-level pyramid network embeds a tiny well-configured contextual pyramid block to extract fine-grained features of small targets. …”
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PCPE-YOLO with a lightweight dynamically reconfigurable backbone for small object detection by Weijia Chen, Jiaming Liu, Tong Liu, Yaoming Zhuang

“…Next, we introduce a Context Anchor Attention mechanism that boosts the model’s focus on the contexts of small objects, thereby improving detection accuracy. In addition, we add a small object detection layer to enhance the model’s localization capability for small objects. …”
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Lightweight Small Target Detection Algorithm Based on YOLOv8 Network Improvement by Xiaoyi Hao, Ting Li

“…Finally, the Std detection layer is integrated into YOLOv8n, thereby enhancing the model’s ability to accurately detect small targets. …”
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Advancing Ton-Bag Detection in Seaport Logistics with an Enhanced YOLOv8 Algorithm by Xiulin Qiu, Haozhi Zhang, Chang Yuan, Qinghua Liu, Hongzhi Yao

“…Finally, the C2f-ORECZ block based on a linear scaling layer is designed for the neck, which reduces the training overhead and strengthens the feature learning of the feature extraction network for the targets in the complex background of the harbor and adds the 160 × 160 scale detection head to strengthen small target detection abilities. …”
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Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features by Hua Yang, Yanjie Lyu, Yunpeng Jiang, Feng Jiang, Taiyong Deng, Lihao Yu, Yuanhao Qiu, Hao Xue, Junying Guo, Zhaoqi Meng

“…To solve the missed and wrong detection problems of the object detection model in identifying soybean companion weeds, this paper proposes an enhanced multi-scale channel feature model based on RT-DETR (EMCF-RTDETR). …”
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Benthos-DETR: a high-precision efficient network for benthic organisms detection by Weibo Rao, Gang Chen, Yifei Zhang, Jue Cang, Shusen Chen, Chenyang Wang

“…This study proposes Benthos-DETR, a benthic organisms detection network based on the RT-DETR network. In the backbone of Benthos-DETR network, the Efficient Block with the C2f module reinforces the shallow feature extraction operation in Benthos-DETR, enhancing the algorithm’s multi-scale perception. …”
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A Configurable Accelerator for CNN-Based Remote Sensing Object Detection on FPGAs by Yingzhao Shao, Jincheng Shang, Yunsong Li, Yueli Ding, Mingming Zhang, Ke Ren, Yang Liu

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Application of VGG16 in Automated Detection of Bone Fractures in X-Ray Images by Resky Adhyaksa, Bedy Purnama

“…The study utilizes the VGG16 architecture, pre-trained on ImageNet, as a base model, with transfer learning applied to adapt the model for fracture detection by fine-tuning its weights. This architecture consists of five blocks of convolutional and max-pooling layers to effectively extract and enhance information from the images for precise classification. …”
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YOLO-SR: An optimized convolutional architecture for robust ship detection in SAR Imagery by Chi Kien Ha, Hoanh Nguyen, Vu Duc Van

“…Concurrently, C2f‐MSDR replaces standard bottleneck layers with multi-scale dilation residual blocks, expanding the receptive field to handle wide variations in ship size. …”
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Detection of fetal congenital heart defects on three-vessel view ultrasound videos by Netzahualcoyotl Hernandez-Cruz, Olga Patey, Bojana Salovic, Divyanshu Mishra, Md Mostafa Kamal Sarker, Aris Papageorghiou, J. Alison Noble

“…The first phase combines three residual networks (ResNets) extended with a self-attention block and a refinement module. The second phase extends a ResNet with two CoordConv layers integrating spatial coordinates. …”
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Towards precision agriculture tea leaf disease detection using CNNs and image processing by Irfan Sadiq Rahat, Hritwik Ghosh, Suresh Dara, Shashi Kant

“…These blocks combine Conv2D layers, batch normalization, activation layers, and shortcut connections, ensuring robust and efficient feature extraction at various levels of abstraction. …”
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