Deep Learning Strategy for UAV-Based Multi-Class Damage Detection on Railway Bridges Using U-Net with Different Loss Functions by Yong-Hyoun Na, Doo-Kie Kim

“…The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. …”
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A Detection and Cover Integrated Waveform Design Method with Good Correlation Characteristics and Doppler Tolerance by Haoting Guo, Fulai Wang, Nanjun Li, Zezhou Wu, Chen Pang, Lei Zhang, Yongzhen Li

“…By constructing a joint optimization model, the sidelobe characteristics of the signal’s autoambiguity function and the output response of the non-cooperative matched filter were incorporated into the unified objective function framework. …”
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Improved YOLO for long range detection of small drones by Sicheng Zhou, Lei Yang, Huiting Liu, Chongqin Zhou, Jiacheng Liu, Yang Wang, Shuai Zhao, Keyi Wang

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Insulator detection based on SE-YOLOv5s by Qing TIAN, Rong HU, Zuoyong LI, Yuanzheng CAI, Zhaochai YU

“…In a large environment where the power system needs to be inspected, the traditional method of manual inspection has great inconvenience and potential safety risks, and the object detection method of unmanned aerial vehicle has great application prospects in the direction of insulator detection and recognition.SE-YOLOv5s, a lightweight insulator detection network that performs efficient detection for this task was presented.Firstly, backbone of YOLOv5s by fusing the SE attention module was strengthen.Then, the position distribution of insulator object was investigated and predefined templates of a priori box by K-means clustering on prior coordinate vectors were generated.Finally, the network by a multitask loss function was trained combined with confidence and position regression task.Furthermore, Mosaic data augmentation was utilized to supplement additional training samples.Experimental results demonstrate that the proposed SE-YOLOv5s significantly outperforms baseline methods at multiple criterions including accuracy, recall rate, detection rate and mean average precision.In comparison with the baselines, the proposed network has a flexible trade-off between robustness and memory overhead and it is a potential approach to promote the power system development.…”
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Diagnostic value of EOS count and serum VEGF in bronchial asthma and their correlation with inflammatory factors and lung function indicators by Longqun Liu, Chenfei Zhang, Jian Xu, Wei Hu

“…Abstract Objective To probe the diagnostic value of direct eosinophils (EOS) count and vascular endothelial growth factor (VEGF) in bronchial asthma (BA) and their correlation with inflammatory factors and lung function indicators. …”
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Flexible Adaptive Marine Predator Algorithm and Its Application in Fault Detection for Wind Turbines by WANG Wen, YI Jiabiao

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Multi-Objective Design and Optimization of Hardware-Friendly Grid-Based Sparse MIMO Arrays by Suleyman Gokhun Tanyer, Paul Dent, Murtaza Ali, Curtis Davis, Senthilkumar Rajagopal, Peter F. Driessen

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Method for Detecting Disorder of a Nonlinear Dynamic Plant by Xuechun Wang, Vladimir Eliseev

“…This paper proposes a new disorder detection method CCF-AE for a scalar dynamic plant based only on its input–output relation using a cross-correlation function and neural network autoencoder. …”
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Research on Fabric Defect Detection Algorithm Based on Lightweight YOLOv7-Tiny by Tang Li, Mei Shunqi, Shi Yishan, Zhou Shi, Zheng Quan, Hongkai Jiang, Xu Qiao, Zhang Zhiming

“…Its objectives are to improve the performance of fabric defect detection against complex backgrounds and to find a balance between the algorithm’s lightweight nature and its accuracy. …”
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YOLO-MMCE algorithm for visual detection of engineering vehicles in high-consequence area by Huimei SUN, Lu LIU, Degang WANG, Taiyong WANG

“…Additionally, to improve the regression accuracy of prediction borders, an Efficient Intersection over Union (EIOU) function was incorporated to calculate the width-height difference between prediction and real borders to replace the aspect ratio, thus further elevating the detection accuracy of the algorithm. …”
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A Lightweight Network for UAV Multi-Scale Feature Fusion-Based Object Detection by Sheng Deng, Yaping Wan

“…To tackle the issues of small target sizes, missed detections, and false alarms in aerial drone imagery, alongside the constraints posed by limited hardware resources during model deployment, a streamlined object detection approach is proposed to enhance the performance of YOLOv8s. …”
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Novel Energy-Aware 3D UAV Path Planning and Collision Avoidance Using Receding Horizon and Optimization-Based Control by Gamil Ahmed, Tarek Sheltami

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Enhanced lightweight infrared object detection algorithm for assistive navigation in visually impaired individuals by Zhimin Bai, Yang Yang, Jian Wang, Zhengyang Li, Jiajun Wang, Chunxin Liu

“…Additionally, the CIoU loss function is refined with Inner‐SIoU, elevating bounding box detection precision. …”
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Low resolution remote sensing object detection with fine grained enhancement and swin transformer by Zhijing Xu, Xin Wang, Kan Huang, Ren Chen

“…Abstract Object detection in remote sensing images is a highly complex and challenging task. …”
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RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR by Hao Zhang, Zheng Ma, Xiang Li

“…By integrating features across multiple scales, it improves object detection accuracy and robustness. Finally, we propose a novel bounding box regression loss function, Focaler-GIoU, which makes the model focus more on difficult samples, improving detection performance for small and overlapping targets. …”
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Research on a UAV-View Object-Detection Method Based on YOLOv7-Tiny by Yuyang Miao, Xihan Wang, Ning Zhang, Kai Wang, Lianhe Shao, Quanli Gao

“…The algorithm’s performance in handling object occlusion and multi-scale detection is enhanced by introducing the VarifocalLoss loss function and improving the feature fusion network to BiFPN. …”
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YOLO-SAG: An improved wildlife object detection algorithm based on YOLOv8n by Lingli Chen, Gang Li, Shunkai Zhang, Wenjie Mao, Mei Zhang

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UCN-YOLOv5: Traffic Sign Object Detection Algorithm Based on Deep Learning by Peilin Liu, Zhaoyang Xie, Taijun Li

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YOLO-PWSL-Enhanced Robotic Fish: An Integrated Object Detection System for Underwater Monitoring by Lingrui Lei, Ying Tang, Weidong Zhang, Quan Tang, Haichi Hao

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Lightweight underwater object detection method based on multi-scale edge information selection by Shaobin Cai, Xin Zhou, Wanchen Cai, Liansuo Wei, Yuchang Mo

“…Abstract Underwater object detection is of great significance to marine ecosystems and underwater biodiversity. …”
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