YOLO-SRW: An Enhanced YOLO Algorithm for Detecting Prohibited Items in X-Ray Security Images by Minwei Chen, Zhixian Zhang, Nian Jiang, Xingxing Li, Xin Zhang

“…Then, we integrate the Shallow Robust Feature Downsampling (SRFD) module to enhance the shallow feature extraction in YOLOv8, enhancing the model’s ability to extract features from low-resolution and feature-sparse targets, thus reducing object information loss. Finally, by combining SCYLLA-IoU (SIoU) and Wise-IoUv3 losses, we design the Wise-SIoU loss function to reduce false negatives and false positives in Prohibited item detection, enhancing the model’s generalization ability. …”
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Determining the Level of Threat in Maritime Navigation Based on the Detection of Small Floating Objects with Deep Neural Networks by Mirosław Łącki

“…Such a solution could function as a decision support system capable of detecting and informing the watch officer or helmsman about possible threats and reducing the risk of overlooking them at a critical moment. …”
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AC-YOLO: A lightweight ship detection model for SAR images based on YOLO11. by Rui He, Dezhi Han, Xiang Shen, Bing Han, Zhongdai Wu, Xiaohu Huang

“…To address these challenges, this paper proposes AC-YOLO, a novel lightweight SAR ship detection model based on YOLO11. Specifically, we design a lightweight cross-scale feature fusion module that adaptively fuses multi-scale feature information, enhancing small target detection while reducing model complexity. …”
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TCE-YOLOv5: Lightweight Automatic Driving Object Detection Algorithm Based on YOLOv5 by Han Wang, Zhenwei Yang, Qiaoshou Liu, Qiang Zhang, Honggang Wang

“…Finally, the EIOU loss function is introduced to measure the overlap between the predicted box and the real box more accurately and improve the detection accuracy. …”
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DV-DETR: Improved UAV Aerial Small Target Detection Algorithm Based on RT-DETR by Xiaolong Wei, Ling Yin, Liangliang Zhang, Fei Wu

“…To achieve this, we introduce three main enhancements: (1) ResNet18 as the backbone network to improve feature extraction and reduce model complexity; (2) the integration of recalibration attention units and deformable attention mechanisms in the neck network to enhance multi-scale feature fusion and improve localization accuracy; and (3) the use of the Focaler-IoU loss function to better handle the imbalanced distribution of target scales and focus on challenging samples. …”
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LCDDN-YOLO: Lightweight Cotton Disease Detection in Natural Environment, Based on Improved YOLOv8 by Haoran Feng, Xiqu Chen, Zhaoyan Duan

“…To address the challenges of detecting cotton pests and diseases in natural environments, as well as the similarities in the features exhibited by cotton pests and diseases, a Lightweight Cotton Disease Detection in Natural Environment (LCDDN-YOLO) algorithm is proposed. …”
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LRA-UNet: A Lightweight Residual Attention Network for SAR Marine Oil Spill Detection by Yu Cai, Jingjing Su, Jun Song, Dekai Xu, Liankang Zhang, Gaoyuan Shen

“…Additionally, we design a joint loss function that incorporates Sobel-based edge information, emphasizing boundary features during training to enhance edge sharpness. …”
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VRU-YOLO: A Small Object Detection Algorithm for Vulnerable Road Users in Complex Scenes by Yunxiang Liu, Yuqing Shi

“…Additionally, a lightweight Optimized Shared Detection Head (OSDH-Head) is introduced, reducing computational complexity while improving detection efficiency. …”
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Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks by Ayan Chatterjee, Vajira Thambawita, Michael A. Riegler, Pal Halvorsen

“…In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. …”
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Improving Circulating Tumor Cell Detection Using Image Synthesis and Transformer Models in Cancer Diagnostics by Shuang Liang, Xue Bai, Yu Gu

“…Effective treatment options are often lacking in advanced stages, making early diagnosis crucial for reducing mortality rates. Circulating tumor cells (CTCs) are a promising biomarker for early detection; however, their automatic detection is challenging due to their heterogeneous size and shape, as well as their scarcity in blood. …”
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Explainable artificial intelligence with temporal convolutional networks for adverse weather condition detection in driverless vehicles by Samah Alzanin

“…The CDAAWD-AVXAI approach improves the safety and reliability of AVs by ensuring robust weather detection. Initially, the presented CDAAWD-AVXAI approach applies image pre-processing by utilizing the median filter (MF) model to reduce noise and enhance the quality of input images. …”
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YOLO-MARS: An Enhanced YOLOv8n for Small Object Detection in UAV Aerial Imagery by Guofeng Zhang, Yanfei Peng, Jincheng Li

“…Experiments conducted on the VisDrone2019 dataset demonstrate that the YOLO-MARS method achieves 40.9% and 23.4% in the mAP50 and mAP50:95 metrics, respectively, representing improvements of 8.1% and 4.3% in detection accuracy compared to the benchmark model YOLOv8n, thus demonstrating its advantages in UAV aerial target detection.…”
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Integrated Sensing and Communication Target Detection Framework and Waveform Design Method Based on Information Theory by Qilong Miao, Xiaofeng Shen, Chenfei Xie, Yong Gao, Lu Chen

“…Target detection is a core function of integrated sensing and communication (ISAC) systems. …”
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The Lightweight Method of Ground Penetrating Radar (GPR) Hidden Defect Detection Based on SESM-YOLO by Yu Yan, Guangxuan Jiao, Minxing Cui, Lei Ni

“…Additionally, the SCSA attention mechanism is introduced before the detection head, enabling precise extraction of defect object features. (3) As a novel loss function, MPDIoU is proposed to reduce the disparity between the corners of the predicted bounding boxes and those of the ground truth boxes. …”
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A Lightweight Deep Learning Network with an Optimized Attention Module for Aluminum Surface Defect Detection by Yizhe Li, Yidong Xie, Hu He

“…Furthermore, we employed the genetic K-means algorithm to optimize prior region selection, and a lightweight Ghost model to reduce network complexity by 14.3%, demonstrating the superior performance of the Ghost model in terms of loss function optimization during training and validation as well as in terms of detection accuracy, speed, and stability. …”
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RMVAD-YOLO: A Robust Multi-View Aircraft Detection Model for Imbalanced and Similar Classes by Keda Li, Xiangyue Zheng, Jingxin Bi, Gang Zhang, Yi Cui, Tao Lei

“…Finally, we propose the WFMIoUv3 loss function, which strengthens the model’s focus on challenging samples and improves detection robustness. …”
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Automated Artery Detection and Stenosis Classification in CTA Using Deep Learning for Peripheral Arterial Disease by Ali M. O. A. Anwer, Hacer Karacan, Muhammed Rabee, Levent Enver, Gonca Cabuk

“…We use Faster R-CNN with a ResNet-101 backbone driven by a custom loss function to achieve good artery localization and reduce false positives. …”
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Lightweight SCD-YOLOv5s: The Detection of Small Defects on Passion Fruit with Improved YOLOv5s by Yu Zhou, Zhenye Li, Sheng Xue, Min Wu, Tingting Zhu, Chao Ni

“…Compared with manual detection, the proposed model enhances detection efficiency by reducing errors caused by subjective judgment. …”
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Infrared Small Target Detection via Multidirectional Local Gravitational Force and Level-Line Connectivity by Xuying Hao, Xianyuan Liu, Yujia Liu, Yijuan Qiu, Yunjing Zhang, Yi Cui, Tao Lei

“…The LGF model integrates information from each pixel within the local region and introduces a new sigmoid function to reduce noise, enabling fine-grained gradient detection. …”
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IRSD-Net: An Adaptive Infrared Ship Detection Network for Small Targets in Complex Maritime Environments by Yitong Sun, Jie Lian

“…Infrared ship detection plays a vital role in maritime surveillance systems. …”
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