DCW-YOLO: Road Object Detection Algorithms for Autonomous Driving by Hongge Ren, Fangke Jing, Song Li

“…Aiming at the problems of multiple parameters and poor detection accuracy of object detection network in automatic driving scenarios, an object detection algorithm based on improved YOLOv8 is proposed. …”
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Improving Tiny Object Detection in Aerial Images with Yolov5 by Ahmed Sharba, Hussain Kanaan

“… Object detection is a major area of computer vision work, particularly for aerial surveillance and traffic control applications, where detecting vehicles from aerial images is essential. …”
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SRM-YOLO for Small Object Detection in Remote Sensing Images by Bin Yao, Chengkun Zhang, Qingxiang Meng, Xiandong Sun, Xuyang Hu, Lu Wang, Xilai Li

“…The model incorporates the following key innovations: Reuse Fusion Structure (RFS), which enhances feature fusion; SPD-Conv, which enables effective downsampling while preserving critical information; and a specialized detection head designed for small objects. Additionally, the MPDIoU loss function is employed to improve detection accuracy. …”
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A Lightweight Citrus Object Detection Method in Complex Environments by Qiurong Lv, Fuchun Sun, Yuechao Bian, Haorong Wu, Xiaoxiao Li, Xin Li, Jie Zhou

“…Experimental results indicate that for the citrus dataset collected in a natural environment, the improved model reduces Params and GFLOPs by 15.4% and 23.7%, respectively, while improving precision, recall, and mAP by 0.3%, 4%, and 3.5%, respectively, thereby outperforming other detection networks. Additionally, an analysis of citrus object detection under varying lighting and occlusion conditions reveals that the YOLO-PBGM network model demonstrates good adaptability, effectively coping with variations in lighting and occlusions while exhibiting high robustness. …”
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A Benchmark Review of YOLO Algorithm Developments for Object Detection by Zhengmao Hua, Kaviya Aranganadin, Cheng-Cheng Yeh, Xinhe Hai, Chen-Yun Huang, Tsan-Chuen Leung, Hua-Yi Hsu, Yung-Chiang Lan, Ming-Chieh Lin

“…You Only Look Once (YOLO) has established itself as a prominent object detection framework due to its excellent balance between speed and accuracy. …”
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Methods for semantic analysis of the state of the process of functioning of a system for detecting, preventing and eliminating the consequences of computer attacks by V. O. Shablya, S. A. Konovalenko, E. O. Orlov

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The value of ⁹⁹Tcm-DTPA renal dynamic imaging for detecting renal function in patients with gouts by LIU Zhi-jun, LI Guo-xiong, TANG Kai, ZHANG Dan

“…Objective To investigate the value of ⁹⁹Tcm-DTPA renal dynamic imaging in patients with gouts.Methods A total of 157 gouts patients（153 male;age range 18⁸8 years） underwent conventional ⁹⁹Tcm-DTPA renal dynamic imaging from June 2011 to October 2014.After bolus injection of 111¹85 MBq tracer,a series of images were obtained.DTPA renograms were generated by the application of computer technology ROIs.GFR were calculated using Gates and C₂₀ were gained also.The GFR detected by ⁹⁹Tcm-DTPA was compared with BUN,SCr and UA.Furthermore,unilateral renal GFR and C₂₀ was analysed.Results BUN and SCr still maintain a normal level when the GFR measurement by renal dynamic imaging decreased slightly.GFR was low as 60 ml/min or less,BUN and SCr increased significantly.The GFR changed earlier than BUN and SCr in gouts patients with renal damage.Excluding the impact of factors such as urinary tract obstruction,19.7%of patients showed unilateral renal GFR decline.Reducing C₂₀ was observed in 22.3%kidneys with normal GFR.UA levels were not changed significantly with worsening renal function.Conclusions Radionuclide renal dynamic imaging is an effective method to assess the renal function in patients with gout.…”
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A method for detection of functional deficiencies due to unilateral vestibular impairment using the TUG test and IMU sensors by Marek Kamiński, Rafał Kotas, Wojciech Tylman, Magdalena Janc, Ewa Zamysłowska-Szmytke, Magdalena Józefowicz-Korczyńska

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Autonomous Driving Road Environment Recognition with Multiscale Object Detection by Jeny J.R.V., Divya Phulari, Varsha Kolanu, Mrunalini Anantha, Irfan S.K.M.

“…Using cutting-edge deep learning techniques, this research presents a novel way for autonomous road environment classification and item detection. It focuses on combining Yolov5 and multiscale small object detection models. …”
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Few-shot learning for novel object detection in autonomous driving by Yifan Zhuang, Pei Liu, Hao Yang, Kai Zhang, Yinhai Wang, Ziyuan Pu

“…Additionally, we design a one-stage object detector for efficient object detection in autonomous driving scenarios. …”
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Remote Sensing Image Detection Method Combining Dynamic Convolution and Attention Mechanism by Yunfei Zhang, Ming Chen, Cong Chen

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Assessment of Brain Function After 240 Days Confinement Using Functional Near Infrared Spectroscopy by Fares Al-Shargie, Usman Tariq, Saleh Al-Ameri, Abdalla Al-Hammadi, Schastlivtseva Daria Vladimirovna, Hasan Al-Nashash

“…In this study, we utilize a diverse set of stress indicators including salivary alpha amylase (sAA) levels, reaction time (RT) to stimuli, accuracy of target detection, and power spectral density (PSD), in conjunction with functional connectivity networks (FCN). …”
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Smart Agricultural Pest Detection Using I-YOLOv10-SC: An Improved Object Detection Framework by Wenxia Yuan, Lingfang Lan, Jiayi Xu, Tingting Sun, Xinghua Wang, Qiaomei Wang, Jingnan Hu, Baijuan Wang

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ARSOD-YOLO: Enhancing Small Target Detection for Remote Sensing Images by Yijuan Qiu, Xiangyue Zheng, Xuying Hao, Gang Zhang, Tao Lei, Ping Jiang

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Lightweight Small Target Detection Algorithm Based on YOLOv8 Network Improvement by Xiaoyi Hao, Ting Li

“…The primary objective of this paper is to address the shortcomings of existing algorithms in the context of UAV-based object detection. …”
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Improved YOLOv8 Object Detection Method for Drone Aerial Images by Zhong Shuai, Wang Liping

“…A new improved YOLOv8 drone aerial image object detection method, referred to as the BDI-YOLO model, is proposed to address the problems of small target object size and blurry feature information in drone aerial images, which can lead to missed and false detections. …”
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Study on lightweight strategies for L-YOLO algorithm in road object detection by Ji Hong, Kuntao Ye, Shubin Qiu

“…Abstract With the increasing complexity of urban traffic, object detection has become critical in autonomous driving and intelligent traffic management. …”
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Detection and tracking of mask wearing based on deep learning by Wang Lin, Nan Gaigai

“…The tracking module adopts the multiple object tracking algorithm Deep SORT to track the detected objects in actual time, which can effectively avoid repeated detection and better the tracking effect of the occluded targets. …”
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Human Body Tracking Method Based on YOLOV5s Object Detection by Shaymaa Tarkan Abdullah, Bashar Talib AL-Nuaimi, Hazim Noman Abed

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Enhanced object detection in low-visibility haze conditions with YOLOv9s. by Yang Zhang, Bin Zhou, Xue Zhao, Xiaomeng Song

“…Low-visibility haze environments, marked by their inherent low contrast and high brightness, present a formidable challenge to the precision and robustness of conventional object detection algorithms. This paper introduces an enhanced object detection framework for YOLOv9s tailored for low-visibility haze conditions, capitalizing on the merits of contrastive learning for optimizing local feature details, as well as the benefits of multiscale attention mechanisms and dynamic focusing mechanisms for achieving real-time global quality optimization. …”
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