Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…In response to the limited research on fire detection in cotton pickers and the issue of low detection accuracy in visual inspection, this paper proposes a computer vision-based detection method. …”
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Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion by Junming Wang, Yuanfeng Gong, Wupeng Deng, Enshun Lu, Xinyu Hu, Daode Zhang

“…Firstly, the backbone network integrates FasterNet-Block, C2f, and an efficient multi-scale EMA attention mechanism to address attention dispersion problems during feature extraction, delivering real-time object detection across different scales. …”
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GEB-YOLO: Optimized YOLOv7 Model for Surface Defect Detection on Aluminum Profiles by Zihao Xu, Jinran Hu, Xingyi Xiao, Yujian Xu

“…In recent years, achieving high-precision and high-speed target detection of surface defects on aluminum profiles to meet the requirements of industrial applications has been challenging. …”
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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

“…Key to our model’s design is the incorporation of residual blocks, facilitating the learning of deeper networks by alleviating the vanishing gradient problem. …”
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Improved Surface Enhanced Raman Scattering Based on Hybrid Au Nanostructures for Biomolecule Detection by Yang Li, Long Zhou, Longhua Tang, Mingyu Li, Jian-Jun He

“…We report the use of highly ordered, dense, and regular arrays of in-plane silicon nanowires as building blocks to produce highly sensitive and well-reproducible surface-enhanced Raman scattering (SERS). …”
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Surface anomaly detection on island-based PV panels using edge neural networks by ZHANG Yinxian, ZHANG Zhanyao, ZHANG Xiya

“…Additionally， a dual dynamic model compression technique is employed to reduce redundant channels and feature blocks， significantly lowering the model’s computational complexity and enabling rapid and accurate anomaly detection. …”
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Deep Learning-Based Video Anomaly Detection Using Optimised Attention-Enhanced Autoencoders by Anjali S, Don S

“…A novel autoencoder (SESAA) is proposed in this work that combines self-attention with squeeze-and-excitation (SE) blocks and improves video anomaly detection by using a thresholding technique for optimal threshold identification. …”
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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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Face Detection Method based on Lightweight Network and Weak Semantic Segmentation Attention Mechanism by Xiaoyan Wu

“…The backbone network structure is improved by introducing Mobile Net lightweight network model, to reduce the number of parameters and calculation of the model and improve the detection speed. The convolutional block attention module model with dual attention mechanism is embedded to improve the sensitivity of the model to target features, which can suppress interference information and improve the accuracy of target detection. …”
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MSSA: multi-stage semantic-aware neural network for binary code similarity detection by Bangrui Wan, Jianjun Zhou, Ying Wang, Feng Chen, Ying Qian

“…Binary code similarity detection (BCSD) aims to identify whether a pair of binary code snippets is similar, which is widely used for tasks such as malware analysis, patch analysis, and clone detection. …”
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Entanglement detection with quantum support vector machine (QSVM) on near-term quantum devices by Mahmoud Mahdian, Zahra Mousavi

“…Abstract Detecting and quantifying quantum entanglement remain significant challenges in the noisy intermediate-scale quantum (NISQ) era. …”
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Revolutionizing Alzheimer’s disease detection with a cutting-edge CAPCBAM deep learning framework by Houmem Slimi, Sabeur Abid, Mounir Sayadi

“…The study’s advantages include improved feature extraction, faster convergence, and superior classification performance, making it a promising tool for the early detection of Alzheimer’s disease.…”
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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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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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STBNA-YOLOv5: An Improved YOLOv5 Network for Weed Detection in Rapeseed Field by Tao Tao, Xinhua Wei

“…To address the issues of dense weed identification, frequent occlusion, and varying weed sizes in rapeseed fields, this paper introduces a STBNA-YOLOv5 weed detection model and proposes three enhanced algorithms: incorporating a Swin Transformer encoder block to bolster feature extraction capabilities, utilizing a BiFPN structure coupled with a NAM attention mechanism module to efficiently harness feature information, and incorporating an adaptive spatial fusion module to enhance recognition sensitivity. …”
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DScanNet: Packaging Defect Detection Algorithm Based on Selective State Space Models by Yirong Luo, Yanping Du, Zhaohua Wang, Jingtian Mo, Wenxuan Yu, Shuihai Dou

“…To address the problem that the model’s detailed feature extraction for small target defects is not sufficient and thus leads to low detection accuracy, the MEFE module, the local feature extraction module (LFEM Block), and the PCR module of the multi-scale convolution and feature enhancement strategy are proposed to enhance the model’s capability of capturing defective features and focusing on specific features, and to improve the detection accuracy. …”
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DDoS-MSCT: A DDoS Attack Detection Method Based on Multiscale Convolution and Transformer by Bangli Wang, Yuxuan Jiang, You Liao, Zhen Li

“…The DDoS-MSCT architecture introduces the DDoS-MSCT block, which consists of a local feature extraction module (LFEM) and a global feature extraction module (GFEM). …”
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Adaptive Anomaly Detection in Network Flows With Low-Rank Tensor Decompositions and Deep Unrolling by Lukas Schynol, Marius Pesavento

“…Anomaly detection (AD) is increasingly recognized as a key component for ensuring the resilience of future communication systems. …”
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Detection of Apple Leaf Gray Spot Disease Based on Improved YOLOv8 Network by Siyi Zhou, Wenjie Yin, Yinghao He, Xu Kan, Xin Li

“…The details are as follows: (1) we introduce Dynamic Residual Blocks (DRBs) to boost the model’s ability to extract lesion features, thereby improving detection accuracy; (2) add a Self-Balancing Attention Mechanism (SBAY) to optimize the feature fusion and improve the ability to deal with complex backgrounds; and (3) incorporate an ultra-small detection head and simplify the computational model to reduce the complexity of the YOLOv8 network while maintaining the high precision of detection. …”
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AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images by Binyao Wang, Ya’nan Zhou, Weiwei Zhu, Li Feng, Jinke He, Tianjun Wu, Jiancheng Luo, Xin Zhang

“…To improve detection accuracy in these contexts, this study proposes AAMS-YOLO, a YOLO-based farmland parcel detection model. …”
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