Enhanced Yolov8 network with Extended Kalman Filter for wildlife detection and tracking in complex environments by Langkun Jiang, Li Wu

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A low-complexity AMP detection algorithm with deep neural network for massive mimo systems by Zufan Zhang, Yang Li, Xiaoqin Yan, Zonghua Ouyang

“…However, existing detection methods have not yet made a good tradeoff between Bit Error Rate (BER) and computational complexity, resulting in slow convergence or high complexity. …”
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A complex roadside object detection model based on multi-scale feature pyramid network by Zhihao Zheng, Jianguang Zhao, Jingjing Fan, Ruirui Bai, Jiana Zhao, Jianan Liu

“…Abstract In response to the challenges of false positives and misses caused by dense occlusions and small targets in complex road environments, this paper proposes an enhanced YOLOv8-based network named YOLO-RC for advanced road traffic object 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

“…To address these issues, we propose an Infrared Ship Detection Network (IRSD-Net), a lightweight and efficient detection network built upon the YOLOv11n framework and specially designed for infrared maritime imagery. …”
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A Comparative Crash-Test of Manual and Semi-Automated Methods for Detecting Complex Submarine Morphologies by Vasiliki Lioupa, Panagiotis Karsiotis, Riccardo Arosio, Thomas Hasiotis, Andrew J. Wheeler

“…A large number of (more than 7000) small but prominent reefs were detected, which made manual mapping extremely time-consuming. …”
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<i>DSW-YOLO</i>-Based Green Pepper Detection Method Under Complex Environments by Yukuan Han, Gaifeng Ren, Jiarui Zhang, Yuxin Du, Guoqiang Bao, Lijun Cheng, Hongwen Yan

“…Experimental results show that on a custom green pepper dataset, <i>DSW-YOLO</i> outperformed the baseline by achieving gains of 2.9%, 2.7%, 2.2%, and 3.4% in P, R, mAP50, and mAP50-95, reducing parameters by 1.6 M, cutting inference time by 0.7 ms, and shrinking the model size to 5.31 MB. <i>DSW-YOLO</i> efficiently and accurately detects green peppers in complex field conditions, significantly improving detection accuracy while remaining lightweight, and provides theoretical and technical support for designing and optimizing pepper-picking robot vision systems.…”
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Hardware and Software Complex for the Study of the Joint Use of Technical Means Based on Various Physical Principles by E. I. Mikhnionok, A. V. Khizniak

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Mitigating Container Damage and Enhancing Operational Efficiency in Global Containerisation by Sergej Jakovlev, Tomas Eglynas, Mindaugas Jusis, Valdas Jankunas, Miroslav Voznak

“…To address these concerns, we present the Impact Detection Methodology (IDM), a system designed to monitor and detect impacts in real time, enhancing operational precision and safety. …”
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GrainNet: efficient detection and counting of wheat grains based on an improved YOLOv7 modeling by Xin Wang, Changchun Li, Chenyi Zhao, Yinghua Jiao, Hengmao Xiang, Xifang Wu, Huabin Chai

“…Additionally, the ASF-Gather and Distribute (ASF-GD) module optimizes the feature extraction component of the original YOLOv7 network, improving the model’s robustness and accuracy in complex scenarios. Ablation experiments validate the effectiveness of the proposed methods.Compared with classic models such as Faster R-CNN, YOLOv5, YOLOv7, and YOLOv8, the GrainNet model achieves better detection performance and computational efficiency in various scenarios and adhesion levels. …”
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YOLO-GL for efficient multi-target detection in large FOV via hierarchical feature fusion by Xinyu Chen, Chengjun Dong, Tong Cui, Guangming Lv, Yan Ren

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YOLOv8n-DDSW: an efficient fish target detection network for dense underwater scenes by Jinwang Yi, Wei Han, Fangfei Lai

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UAV-DETR: An Enhanced RT-DETR Architecture for Efficient Small Object Detection in UAV Imagery by Yu Zhou, Yan Wei

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Towards Efficient SAR Ship Detection: Multi-Level Feature Fusion and Lightweight Network Design by Wei Xu, Zengyuan Guo, Pingping Huang, Weixian Tan, Zhiqi Gao

“…Thus, guided by the principles of lightweight design, robustness, and energy efficiency optimization, this study proposes a three-stage collaborative multi-level feature fusion framework to reduce model complexity without compromising detection performance. …”
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Efficient wildlife monitoring: Deep learning-based detection and counting of green turtles in coastal areas by Naoya Noguchi, Hideaki Nishizawa, Taro Shimizu, Junichi Okuyama, Shohei Kobayashi, Kazuyuki Tokuda, Hideyuki Tanaka, Satomi Kondo

“…However, accurately detecting animals remains challenging, especially in complex field environments. …”
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Investigation of an Efficient Multi-Class Cotton Leaf Disease Detection Algorithm That Leverages YOLOv11 by Fangyu Hu, Mairheba Abula, Di Wang, Xuan Li, Ning Yan, Qu Xie, Xuedong Zhang

“…By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. …”
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Reliable and efficient magnetic data inversion for resource detection using a hybrid bat algorithm by Khalid S. Essa, Mahmoud Elhussein, Omar A. Gomaa, Zein E. Diab

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Enhancing Real-Time Road Object Detection: The RD-YOLO Algorithm With Higher Precision and Efficiency by Weijian Wang, Wei Yu

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CSCP-YOLO: A Lightweight and Efficient Algorithm for Real-Time Steel Surface Defect Detection by Chenglong Wang, Heng Wang, Yimin Jiang, Lei Yu, Xueting Wang

“…Steel surface defect detection is critical for ensuring product quality, yet remains challenging due to multi-scale defects, small target prevalence, and complex background interference. …”
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YOLO-Air: An Efficient Deep Learning Network for Small Object Detection in Drone-Based Imagery by Jigang Qiu, Fangkai Cai, Ning Fu, Yuanfei Yao

“…However, it poses unique challenges for object detection due to small objects, complex backgrounds, and noise interference. …”
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A Lightweight Cotton Field Weed Detection Model Enhanced with EfficientNet and Attention Mechanisms by Lu Zheng, Lyujia Long, Chengao Zhu, Mengmeng Jia, Pingting Chen, Jun Tie

“…The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. …”
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