Detecting Planting Holes Using Improved YOLO-PH Algorithm with UAV Images by Kaiyuan Long, Shibo Li, Jiangping Long, Hui Lin, Yang Yin

“…To address this issue, a target detection network named YOLO-PH was designed to efficiently and rapidly detect planting holes in complex environments. …”
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A Lightweight Model for Shine Muscat Grape Detection in Complex Environments Based on the YOLOv8 Architecture by Changlei Tian, Zhanchong Liu, Haosen Chen, Fanglong Dong, Xiaoxiang Liu, Cong Lin

“…This study addresses these challenges by proposing a lightweight YOLOv8-based model, incorporating DualConv and the novel C2f-GND module to enhance feature extraction and reduce computational complexity. Evaluated on the newly developed Shine-Muscat-Complex dataset of 4715 images, the proposed model achieved a 2.6% improvement in mean Average Precision (mAP) over YOLOv8n while reducing parameters by 36.8%, FLOPs by 34.1%, and inference time by 15%. …”
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YOLO-SBA: A Multi-Scale and Complex Background Aware Framework for Remote Sensing Target Detection by Yifei Yuan, Yingmei Wei, Xiaoyan Zhou, Yanming Guo, Jiangming Chen, Tingshuai Jiang

“…Remote sensing target detection faces significant challenges in handling multi-scale targets, with the high similarity in color and shape between targets and backgrounds in complex scenes further complicating the detection task. …”
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CGADNet: A Lightweight, Real-Time, and Robust Crosswalk and Guide Arrow Detection Network for Complex Scenes by Guangxing Wang, Tao Lin, Xiwei Dong, Longchun Wang, Qingming Leng, Seong-Yoon Shin

“…In this study, we incorporated a novel C2f_Van module based on VanillaBlock, employed depth-separable convolution to reduce the parameters efficiently, utilized partial convolution (PConv) for lightweight FasterDetect, and utilized a bounding box regression loss with a dynamic focusing mechanism—WIoU<i>_v3</i>—to enhance the detection performance. …”
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DAF-Net: Dual-Aperture Feature Fusion Network for Aircraft Detection on Complex-Valued SAR Image by Qingbiao Meng, Youming Wu, Yuxi Suo, Tian Miao, Qingyang Ke, Xin Gao, Xian Sun

“…Aircraft detection in synthetic aperture radar (SAR) images plays a crucial role in supporting essential tasks, such as airport management and airspace monitoring. …”
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Eye contact based engagement prediction for efficient human–robot interaction by Magnus Jung, Ahmed Abdelrahman, Thorsten Hempel, Basheer Al-Tawil, Qiaoyue Yang, Sven Wachsmuth, Ayoub Al-Hamadi

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EMB-YOLO: A Lightweight Object Detection Algorithm for Isolation Switch State Detection by Haojie Chen, Lumei Su, Riben Shu, Tianyou Li, Fan Yin

“…This module is designed with a lightweight structure, aimed at reducing the computational complexity and parameter count, thereby optimizing the model’s computational efficiency. …”
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Field Trial Transmission of Time Frequency Packed DP-QPSK Superchannel With Spectral Efficiency of 6.2 bit/s/Hz by Gianluca Meloni

“…The use of low-order modulation format guarantees better robustness in terms of optical-signal-to-noise ratio (OSNR) and reduced complexity with respect to higher order formats. At the receiver side, coherent detection was used, together with iterative maximum <bold> <italic>a posteriori</italic></bold> probability (MAP) detection and decoding. …”
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Attention-Based Target Detection–You Only Look Once: A Detection Model for <i>Locusta migratoria</i> ssp. <i>manilensis</i> in Complex Environments by Peng Wang, Jiandong Fang, Xiuling Wang, Yudong Zhao

“…Aiming at the problem of false detection and missed detection caused by locust occlusion and background similarity in complex field environments, this paper proposes a lightweight Attention-based Target Detection (ATD) model while constructing the dataset Real-Locust with the theme of <i>Locusta migratoria</i> ssp. …”
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FLE-YOLO: A Faster, Lighter, and More Efficient Strategy for Autonomous Tower Crane Hook Detection by Xin Hu, Xiyu Wang, Yashu Chang, Jian Xiao, Hongliang Cheng, Firdaousse Abdelhad

“…To address the complexities of crane hook operating environments, the challenges faced by large-scale object detection algorithms on edge devices, and issues such as frame rate mismatch causing image delays, this paper proposes a faster, lighter, and more efficient object detection algorithm called FLE-YOLO. …”
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Efficient vision transformers with edge enhancement for robust small target detection in drone-based remote sensing by Xuguang Zhu, Zhizhao Zhang

“…This study proposes a lightweight transformer-based detector, MLD-DETR, which enhances detection performance in complex scenarios through multi-scale edge enhancement and hierarchical attention mechanisms. …”
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GES-YOLO: A Light-Weight and Efficient Method for Conveyor Belt Deviation Detection in Mining Environments by Hongwei Wang, Ziming Kou, Yandong Wang

“…The core of this algorithm is to enhance the model’s ability to extract features in complex scenarios, thereby improving the detection efficiency. …”
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A study on an efficient citrus Huanglong disease detection algorithm based on three-channel aggregated attention by Yizong Wang, Zhengrong Xiao, Hong Wang, Fei Li, Jiya Tian

“…Background Aiming at the problems of complex and diverse field symptoms of citrus Huanglong disease (HLB), low efficiency and insufficient recognition accuracy of traditional detection methods, this study proposes an efficient detection algorithm based on improved You Only Look Once (YOLO)v8. …”
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Efficient deep learning-based tomato leaf disease detection through global and local feature fusion by Hao Sun, Rui Fu, Xuewei Wang, Yongtang Wu, Mohammed Abdulhakim Al-Absi, Zhenqi Cheng, Qian Chen, Yumei Sun

“…Abstract In the context of intelligent agriculture, tomato cultivation involves complex environments, where leaf occlusion and small disease areas significantly impede the performance of tomato leaf disease detection models. …”
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MXT-YOLOv7t: An Efficient Real-Time Object Detection for Autonomous Driving in Mixed Traffic Environments by Afdhal Afdhal, Khairun Saddami, Mirshal Arief, Sugiarto Sugiarto, Zahrul Fuadi, Nasaruddin Nasaruddin

“…To address this problem, we present the MXT-Dataset, a novel dataset that captures the complexities of real-world mixed traffic scenarios. We also propose MXT-YOLOv7t, a real-time object detection model designed to efficiently and effectively handle the various challenges in mixed traffic scenarios. …”
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Apple Pest and Disease Detection Network with Partial Multi-Scale Feature Extraction and Efficient Hierarchical Feature Fusion by Weihao Bao, Fuquan Zhang

“…To address this issue, this study proposes an improved pest and disease detection algorithm, YOLO-PEL, based on YOLOv11, which integrates multiple innovative modules, including PMFEM, EHFPN, and LKAP, combined with data augmentation strategies, significantly improving detection accuracy and efficiency in complex environments. …”
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Efficient Structural Damage Detection with Minimal Input Data: Leveraging Fewer Sensors and Addressing Model Uncertainties by Fredi Alegría, Eladio Martínez, Claudia Cortés-García, Quirino Estrada, Andrés Blanco-Ortega, Mario Ponce-Silva

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An efficient algorithm for pedestrian fall detection in various image degradation scenarios based on YOLOv8n by Jianhui Xun, Xuefeng Wang, Xiufang Wang, Xiaoliang Fan, Peishuai Yang, Zhifei Zhang

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LRDS-YOLO enhances small object detection in UAV aerial images with a lightweight and efficient design by Yuqi Han, Chengcheng Wang, Hui Luo, Huihua Wang, Zaiqing Chen, Yuelong Xia, Lijun Yun

“…Abstract Small object detection in UAV aerial images is challenging due to low contrast, complex backgrounds, and limited computational resources. …”
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Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion by Xingyu Di, Kangning Cui, Rui-Feng Wang

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