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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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

“…To address these challenges, this study proposes an efficient Tomato Disease Detection Network (E-TomatoDet), which enhances tomato leaf disease detection effectiveness by integrating and amplifying global and local feature perception capabilities. …”
Get full text

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. …”
Get full text

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

Get full text

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

Get full text

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. …”
Get full text

Toward Efficient UAV-Based Small Object Detection: A Lightweight Network with Enhanced Feature Fusion by Xingyu Di, Kangning Cui, Rui-Feng Wang

Get full text

YOLORM: An Advanced Key Point Detection Method for Accurate and Efficient Rotameter Reading in Low Flow Environments by Huang Yong, Xia Xing, Xiao Shengwang

“…Automatic reading of rotameters in low flow and challenging environments poses substantial accuracy and efficiency challenges. To address these issues, this study introduces YOLORM, an advanced key point detection method for rotameters, built upon the YOLOv8n model. …”
Get full text

SqueezeSlimU-Net: An Adaptive and Efficient Segmentation Architecture for Real-Time UAV Weed Detection by Alina L. Machidon, Andraz Krasovec, Veljko Pejovic, Octavian M. Machidon

“…In this article, we introduce SqueezeSlimU-Net (SSU-Net), an adaptive and efficient deep learning (DL) model designed to enhance UAV capabilities in performing complex image segmentation tasks under resource constraints, thereby advancing real-time UAV vision—a crucial technology in fields, such as precision agriculture. …”
Get full text

Enhanced Grounding DINO: Efficient Cross-Modality Block for Open-Set Object Detection in Remote Sensing by Zibo Hu, Kun Gao, Jingyi Wang, Zhijia Yang, Zefeng Zhang, Haobo Cheng, Wei Li

“…The efficient cross-modality block reduces the computational complexity of both multiscale visual feature refinement and the fusion of text and visual features, while maintaining model performance. …”
Get full text

WCANet: An Efficient and Lightweight Weight Coordinated Adaptive Detection Network for UAV Inspection of Transmission Line Accessories by Jiawei Chen, Pengfei Shi, Mengyao Xu, Yuanxue Xin, Xinnan Fan, Jinbo Zhang

“…Existing network models suffer from issues like low precision in accessory detection, elevated model complexity, and a narrow range of category detection, especially in UAV-based inspection scenarios. …”
Get full text

Lights-Transformer: An Efficient Transformer-Based Landslide Detection Model for High-Resolution Remote Sensing Images by Xu Wu, Xuqing Ren, Donghao Zhai, Xiangpeng Wang, Mehreen Tarif

“…However, existing models often face challenges, such as incomplete feature extraction, loss of contextual information, and high computational complexity. To overcome these challenges, we propose an innovative landslide detection model, Lights-Transformer, which is designed to improve both the accuracy and efficiency. …”
Get full text

Coherent Detection of Non-Orthogonal Spectrally Efficient Multicarrier Signals Using a Decision Feedback Algorithm by S. B. Makarov, S. V. Zavjalov, D. C. Nguyen, A. S. Ovsyannikova

“…At the same time, the efficiency of the detection algorithm with decision feedback turns out to be significantly lower than that when using the detection algorithm MLSE.Conclusion. …”
Get full text

A Lightweight Multi-Scale Context Detail Network for Efficient Target Detection in Resource-Constrained Environments by Kaipeng Wang, Guanglin He, Xinmin Li

“…Moreover, the need for solutions suitable for edge computing environments, which have limited computational resources, adds complexity to the task. To meet these challenges, we propose MSCDNet (Multi-Scale Context Detail Network), an innovative and lightweight architecture designed specifically for efficient target detection in such environments. …”
Get full text

PVD-GSTPS: design of an efficient parallel vehicle detection based green signal time prediction system by Nikhil Nigam, Dhirendra Pratap Singh, Jaytrilok Choudhary, Surendra Solanki

Get full text

MultiDistiller: Efficient Multimodal 3D Detection via Knowledge Distillation for Drones and Autonomous Vehicles by Binghui Yang, Tao Tao, Wenfei Wu, Yongjun Zhang, Xiuyuan Meng, Jianfeng Yang

“…Although significant progress has been made in detection methods based on point clouds, cameras, and multimodal fusion, the computational complexity of existing high-precision models struggles to meet the real-time requirements of vehicular edge devices. …”
Get full text

An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids by Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín, Sarvenaz Sadat Khatami

“…The increasing complexity of modern smart power distribution systems (SPDSs) has made anomaly detection a significant challenge, as these systems generate vast amounts of heterogeneous and time-dependent data. …”
Get full text

Streamlining Deep Learning Network for Real-time Sea Turtle Detection by Muhamad Dwisnanto Putro, Yuliana Mose, Alex Copernikus Andaria, Jane Litouw, Vecky Canisius Poekoel, Xaverius Najoan

“…A new turtle dataset is proposed that contains lighting, blur, occlusion, and complex background challenges. The evaluation results show that the proposed model performs higher accuracy than other lightweight object detection models. …”
Get full text