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Journal articles on the topic 'Object detection in images'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

Shin, Su-Jin, Seyeob Kim, Youngjung Kim, and Sungho Kim. "Hierarchical Multi-Label Object Detection Framework for Remote Sensing Images." Remote Sensing 12, no. 17 (2020): 2734. http://dx.doi.org/10.3390/rs12172734.

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Detecting objects such as aircraft and ships is a fundamental research area in remote sensing analytics. Owing to the prosperity and development of CNNs, many previous methodologies have been proposed for object detection within remote sensing images. Despite the advance, using the object detection datasets with a more complex structure, i.e., datasets with hierarchically multi-labeled objects, is limited to the existing detection models. Especially in remote sensing images, since objects are obtained from bird’s-eye view, the objects are captured with restricted visual features and not always

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Moitra, Sabyasachi, and Sambhunath Biswas. "Object Detection in Images: A Survey." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 10–29. http://dx.doi.org/10.21275/sr23330184650.

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Jung, Sejung, Won Hee Lee, and Youkyung Han. "Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor’s Elevation and Azimuth Angles." Remote Sensing 13, no. 18 (2021): 3660. http://dx.doi.org/10.3390/rs13183660.

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Building change detection is a critical field for monitoring artificial structures using high-resolution multitemporal images. However, relief displacement depending on the azimuth and elevation angles of the sensor causes numerous false alarms and misdetections of building changes. Therefore, this study proposes an effective object-based building change detection method that considers azimuth and elevation angles of sensors in high-resolution images. To this end, segmentation images were generated using a multiresolution technique from high-resolution images after which object-based building

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Wang, Zhiyuan, Shujun Men, Yuntian Bai, et al. "Improved Small Object Detection Algorithm CRL-YOLOv5." Sensors 24, no. 19 (2024): 6437. http://dx.doi.org/10.3390/s24196437.

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Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module i

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Vajda, Peter, Ivan Ivanov, Lutz Goldmann, Jong-Seok Lee, and Touradj Ebrahimi. "Robust Duplicate Detection of 2D and 3D Objects." International Journal of Multimedia Data Engineering and Management 1, no. 3 (2010): 19–40. http://dx.doi.org/10.4018/jmdem.2010070102.

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In this paper, the authors analyze their graph-based approach for 2D and 3D object duplicate detection in still images. A graph model is used to represent the 3D spatial information of the object based on the features extracted from training images to avoid explicit and complex 3D object modeling. Therefore, improved performance can be achieved in comparison to existing methods in terms of both robustness and computational complexity. Different limitations of this approach are analyzed by evaluating performance with respect to the number of training images and calculation of optimal parameters

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Sejr, Jonas Herskind, Peter Schneider-Kamp, and Naeem Ayoub. "Surrogate Object Detection Explainer (SODEx) with YOLOv4 and LIME." Machine Learning and Knowledge Extraction 3, no. 3 (2021): 662–71. http://dx.doi.org/10.3390/make3030033.

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Due to impressive performance, deep neural networks for object detection in images have become a prevalent choice. Given the complexity of the neural network models used, users of these algorithms are typically given no hint as to how the objects were found. It remains, for example, unclear whether an object is detected based on what it looks like or based on the context in which it is located. We have developed an algorithm, Surrogate Object Detection Explainer (SODEx), that can explain any object detection algorithm using any classification explainer. We evaluate SODEx qualitatively and quan

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Wu, Jingqian, and Shibiao Xu. "From Point to Region: Accurate and Efficient Hierarchical Small Object Detection in Low-Resolution Remote Sensing Images." Remote Sensing 13, no. 13 (2021): 2620. http://dx.doi.org/10.3390/rs13132620.

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Accurate object detection is important in computer vision. However, detecting small objects in low-resolution images remains a challenging and elusive problem, primarily because these objects are constructed of less visual information and cannot be easily distinguished from similar background regions. To resolve this problem, we propose a Hierarchical Small Object Detection Network in low-resolution remote sensing images, named HSOD-Net. We develop a point-to-region detection paradigm by first performing a key-point prediction to obtain position hypotheses, then only later super-resolving the

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Vu, Thang C., Thanh V. Nguyen, Tao V. Nguyen, et al. "Object Detection in Remote Sensing Images Using Deep Learning: From Theory to Applications in Intelligent Transportation Systems." Journal of Future Artificial Intelligence and Technologies 2, no. 2 (2025): 227–41. https://doi.org/10.62411/faith.3048-3719-114.

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Object detection for sensing images is one of the promising research directions in computer vision. Applications for object detection from remote sensing images play an important role in analyzing aerial or satellite imagery. Benefits include applications in monitoring buildings and infrastructure, transportation, supporting search and rescue or responding to natural disasters, and environmental research. However, detecting objects in remote sensing images is difficult due to the diversity of shapes and sizes, viewing angles of objects, and complex background environments. In this paper, the a

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Fidelis, Nfwan Gonten. "Improved Fast YOLO One-Stage Object Detection Algorithm for Detecting Objects in Images." International Journal of Inventive Engineering and Sciences (IJIES) 12, no. 5 (2025): 1–8. https://doi.org/10.35940/ijies.D4597.12050525.

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<strong>Abstract: </strong>The use of a Convolutional neural network (CNN) has gained wide recommendation in the research community, especially in the area of vision systems (Object detection). The recent CNN recorded Various advancements in object detection in images with tremendous accuracy but still faced challenges of high time complexity. A one-stage object detection algorithm called YOLO (You Only Look Once), used for object classification and localization, performs great, especially in detecting objects in real time. In this study, we proposed an improved, fast YOLO CNN-based algorithm

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T. Sanjeeva Kumar. "Moving Object Detection in Aerial Images using DeepSORT with Faster R-CNN." Journal of Information Systems Engineering and Management 10, no. 4s (2025): 391–404. https://doi.org/10.52783/jisem.v10i4s.531.

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Aerial imagery is increasingly utilized in various applications, including surveillance, disaster management, agriculture, and urban planning. Detecting and tracking moving objects within aerial images is a crucial task for these applications. This paper presents a novel approach to moving object detection in aerial images, combining the Faster R-CNN (Region-based Convolutional Neural Network) for object detection and the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm for object tracking. The proposed method leverages the strengths of both techniques, enabling accurate and effic

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Yehia, Amany, and Shereen A. Taie. "RGB-D and corrupted images in assistive blind systems in smart cities." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 1970–82. http://dx.doi.org/10.11591/eei.v11i4.3770.

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Assistive blind systems or assistive systems for visually impaired in smart cities help visually impaired to perform their daily tasks faced two problems when using you only look once version 3 (YOLOv3) object detection. Object recognition is a significant technique used to recognize objects with different technologies, algorithms, and structures. Object detection is a computer vision technique that identifies and locates instances of objects in images or videos. YOLOv3 is the most recent object detection technique that introduces promising results. YOLOv3 object detection task is to determine

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Amany, Yehia, and A. Taie Shereen. "RGB-D and corrupted images in assistive blind systems in smart cities." Bulletin of Electrical Engineering and Informatics 11, no. 4 (2022): 1970~1982. https://doi.org/10.11591/eei.v11i4.3770.

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Assistive blind systems or assistive systems for visually impaired in smart cities help visually impaired to perform their daily tasks faced two problems when using you only look once version 3 (YOLOv3) object detection. Object recognition is a significant technique used to recognize objects with different technologies, algorithms, and structures. Object detection is a computer vision technique that identifies and locates instances of objects in images or videos. YOLOv3 is the most recent object detection technique that introduces promising results. YOLOv3 object detection task is to determine

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Dawn, Wilson, Manusankar C. Dr., and Prathibha P. H. Dr. "Analytical Study on Object Detection using Yolo Algorithm." International Journal of Innovative Science and Research Technology 7, no. 8 (2022): 587–89. https://doi.org/10.5281/zenodo.7036535.

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Object detection is a technique that allows detecting and locating objects in videos and images. Object detection is widely used to count objects in a scene, track their precise locations and accurately label the objects. It seeks to answer what is the object? and Where is it? . Object detection adopts various approaches such as fast R-CNN, Retina-Net, Single Shot MultiBox Detector (SSD) and YOLO. Among these, YOLO is the most powerful algorithm for object detection and as well as suited for real-time scenarios. It is popular because of its accuracy and speed. YOLO uses Neural networks to prov

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Yao, Bin, Chengkun Zhang, Qingxiang Meng, et al. "SRM-YOLO for Small Object Detection in Remote Sensing Images." Remote Sensing 17, no. 12 (2025): 2099. https://doi.org/10.3390/rs17122099.

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Small object detection presents significant challenges in computer vision, often affected by factors such as low resolution, dense object distribution, and complex backgrounds, which can lead to false positives or missed detections. In this paper, we introduce SRM-YOLO, a novel small object detection algorithm based on the YOLOv8 framework. 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 obje

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Pandey, Vishal, Khushboo Anand, Anmol Kalra, Anmol Gupta, Partha Pratim Roy, and Byung-Gyu Kim. "Enhancing object detection in aerial images." Mathematical Biosciences and Engineering 19, no. 8 (2022): 7920–32. http://dx.doi.org/10.3934/mbe.2022370.

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<abstract><p>Unmanned Aerial Vehicles have proven to be helpful in domains like defence and agriculture and will play a vital role in implementing smart cities in the upcoming years. Object detection is an essential feature in any such application. This work addresses the challenges of object detection in aerial images like improving the accuracy of small and dense object detection, handling the class-imbalance problem, and using contextual information to boost the performance. We have used a density map-based approach on the drone dataset VisDrone-2019 accompanied with increased r

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Chaudhari, Nachiket. "Moving Object Detection using Intelligent Spectaculars." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 3342–46. http://dx.doi.org/10.22214/ijraset.2023.52310.

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Abstract: Moving Object detection systems are used to identify and locate objects in images or videos. When used spectacles, an object detection system can allow the user to see information about the objects in their field of view. This can be useful in a variety of applications, such as helping blind people navigate their environment, or providing augmented reality information to workers. Region-based Convolutional Neural Networks (RCNN) is a type of machine learning model that is commonly used for object detection. The RCNN model first uses a convolutional neural network (CNN) to extract fea

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Subagja, Mifta, and Ben Rahman. "Object Detection Using YOLOv5 and OpenCV." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 13, no. 1 (2025): 155–62. https://doi.org/10.33558/piksel.v13i1.10772.

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Object detection is one of the main tasks in computer vision, aimed at recognizing and localizing objects in images or videos. In this study, we utilize the YOLOv5 model, which is well known for its efficiency in realtime object detection. We implement this method with the help of the OpenCV library for image processing. This research aims to evaluate the performance of YOLOv5 in detecting objects in various types of images, including landscape photos, cat photos, and traffic light images with vehicles. The model is trained using optimization methods with the Adam optimizer and assessed throug

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Gonten, Fidelis Nfwan, Dr Ezekwe, Chinwe Genevra, and Otene Patience Unekwuojo. "Improved Fast YOLO One-Stage Object Detection Algorithm for Detecting Objects in Images." International Journal of Inventive Engineering and Sciences 12, no. 5 (2025): 1–8. https://doi.org/10.35940/ijies.d4597.12050525.

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Using a Convolutional neural network (CNN) has gained wide recommendation in the research community, especially in vision systems (Object detection). Recently, CNN recorded various advancements in object detection in images with tremendous accuracy, but they still faced challenges of high time complexity. A one-stage object detection algorithm, YOLO (You Only Look Once), used for object classification and localization, performs great, especially in detecting objects in real time. In this study, we proposed an improved, fast YOLO CNN-based algorithm for detecting objects in images. We introduce

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Fidelis, Nfwan Gonten. "Improved Fast YOLO One-Stage Object Detection Algorithm for Detecting Objects in Images." International Journal of Inventive Engineering and Sciences (IJIES) 12, no. 5 (2025): 1–8. https://doi.org/10.35940/ijies.D4597.12050525/.

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<strong>Abstract: </strong>Using a Convolutional neural network (CNN) has gained wide recommendation in the research community, especially in vision systems (Object detection). Recently, CNN recorded various advancements in object detection in images with tremendous accuracy, but they still faced challenges of high time complexity. A one-stage object detection algorithm, YOLO (You Only Look Once), used for object classification and localization, performs great, especially in detecting objects in real time. In this study, we proposed an improved, fast YOLO CNN-based algorithm for detecting obje

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Wang, Jian, Yuesong Zhang, Fei Zhang, Yazhou Li, Lingcong Nie, and Jiale Zhao. "MegaDetectNet: A Fast Object Detection Framework for Ultra-High-Resolution Images." Electronics 12, no. 18 (2023): 3737. http://dx.doi.org/10.3390/electronics12183737.

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Addressing the challenge of efficiently detecting objects in ultra-high-resolution images during object detection tasks, this paper proposes a novel method called MegaDetectNet, which leverages foreground image for large-scale resolution image object detection. MegaDetectNet utilizes a foreground extraction network to generate a foreground image that highlights target regions, thus avoiding the computationally intensive process of dividing the image into multiple sub-images for detection, and significantly improving the efficiency of object detection. The foreground extraction network in MegaD

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Mamta, A. Baitule* Prof. Mukund R. Joshi. "OBJECT DETECTION AND TRACKING ALGORITHM FOR LOW VISION VIDEO." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 661–65. https://doi.org/10.5281/zenodo.51857.

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We propose a general framework for Object Recognition into regions and objects. In this framework, the detection and recognition of objects proceed simultaneously with image segmentation in a competitive and cooperative manner .Videos are a collection of sequential images with a constant time interval. So video can provide more information about our object when scenarios are changing with respect to time. Therefore, manually handling videos are quite impossible. So we need an automated devise to process these videos. Object tracking is  a process of segmenting a region of interest from a

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Zhang, Bo, ShaoMing Hu, Junju Du, et al. "Detecting Moving Objects in Photometric Images Using 3D Hough Transform." Publications of the Astronomical Society of the Pacific 136, no. 5 (2024): 054502. http://dx.doi.org/10.1088/1538-3873/ad481f.

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Abstract In response to the exponential growth of space debris, an increasing number of observation devices are being used for the observation of moving objects, such as space debris and asteroids, which require further improvements in data-processing capabilities for the detection of moving objects. In this study, we propose a rapid detection algorithm designed for detecting moving objects, leveraging the power of the 3D Hough transform. By the simulated image experiments, our results show that the detection rate increases with the number of continuous images when fully extracting objects. Ba

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Shen, Jie, Zhenxin Xu, Zhe Chen, Huibin Wang, and Xiaotao Shi. "Optical Prior-Based Underwater Object Detection with Active Imaging." Complexity 2021 (April 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/6656166.

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Underwater object detection plays an important role in research and practice, as it provides condensed and informative content that represents underwater objects. However, detecting objects from underwater images is challenging because underwater environments significantly degenerate image quality and distort the contrast between the object and background. To address this problem, this paper proposes an optical prior-based underwater object detection approach that takes advantage of optical principles to identify optical collimation over underwater images, providing valuable guidance for extra

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Liu, Wei, Dayu Cheng, Pengcheng Yin, et al. "Small Manhole Cover Detection in Remote Sensing Imagery with Deep Convolutional Neural Networks." ISPRS International Journal of Geo-Information 8, no. 1 (2019): 49. http://dx.doi.org/10.3390/ijgi8010049.

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With the development of remote sensing technology and the advent of high-resolution images, obtaining data has become increasingly convenient. However, the acquisition of small manhole cover information still has shortcomings including low efficiency of manual surveying and high leakage rate. Recently, deep learning models, especially deep convolutional neural networks (DCNNs), have proven to be effective at object detection. However, several challenges limit the applications of DCNN in manhole cover object detection using remote sensing imagery: (1) Manhole cover objects often appear at diffe

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Yan, Longbin, Min Zhao, Xiuheng Wang, Yuge Zhang, and Jie Chen. "Object Detection in Hyperspectral Images." IEEE Signal Processing Letters 28 (2021): 508–12. http://dx.doi.org/10.1109/lsp.2021.3059204.

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Karimanzira, Divas, Helge Renkewitz, David Shea, and Jan Albiez. "Object Detection in Sonar Images." Electronics 9, no. 7 (2020): 1180. http://dx.doi.org/10.3390/electronics9071180.

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The scope of the project described in this paper is the development of a generalized underwater object detection solution based on Automated Machine Learning (AutoML) principles. Multiple scales, dual priorities, speed, limited data, and class imbalance make object detection a very challenging task. In underwater object detection, further complications come in to play due to acoustic image problems such as non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation, and multipath problems. Therefore, we focus on finding solutions to the problems

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Lorencs, Aivars, Ints Mednieks, and Juris Siņica-Siņavskis. "Fast object detection in digital grayscale images." Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences. 63, no. 3 (2009): 116–24. http://dx.doi.org/10.2478/v10046-009-0026-5.

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Fast object detection in digital grayscale images The problem of specific object detection in digital grayscale images is considered under the following conditions: relatively small image fragments can be analysed (a priori information about the size of objects is available); images contain a varying undefined background (clutter) of larger objects; processing time should be minimised and must be independent from the image contents; proposed methods should provide for efficient implementation in application-specific electronic circuits. The last two conditions reflect the aim to propose approa

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Trần, Minh T., Bao V. Tran, Nguyen D. Vo, and Khang Nguyen. "An object detection method for aerial hazy images." Can Tho University Journal of Science 14, no. 1 (2022): 91–98. http://dx.doi.org/10.22144/ctu.jen.2022.010.

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Image processing and object detection in aerial images have to deal with a lot of trouble due to the existence of haze, smoke, dust in the atmosphere. These factors can blur objects and severely decline image quality which might lead to incorrect or missing object detection. To solve this problem, this study shows a method that can reduce the bad effect of haze on object detection in aerial images. A combination of a dehazing method called Feature Fusion Attention Network (FFA-Net) and an object detection method named Probabilistic Anchor Assignment (PAA) was conducted to evaluate two hypothes

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Li, Yiran, Han Xie, and Hyunchul Shin. "3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds." Signals 2, no. 1 (2021): 98–107. http://dx.doi.org/10.3390/signals2010009.

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Three-dimensional (3D) object detection is essential in autonomous driving. Three-dimensional (3D) Lidar sensor can capture three-dimensional objects, such as vehicles, cycles, pedestrians, and other objects on the road. Although Lidar can generate point clouds in 3D space, it still lacks the fine resolution of 2D information. Therefore, Lidar and camera fusion has gradually become a practical method for 3D object detection. Previous strategies focused on the extraction of voxel points and the fusion of feature maps. However, the biggest challenge is in extracting enough edge information to de

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Liubchenko, Vira, and Dmytro Moroz. "Models and methods of object detection in digital image processing." Bulletin of the National Technical University "KhPI" A series of "Information and Modeling" 1, no. 1-2 (11-12) (2024): 61–74. http://dx.doi.org/10.20998/2411-0558.2024.01.06.

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Object detection in digital images can face such problems that arise in the process of image registration as the presence of noise, low image quality/resolution, illumination heterogeneity, overlapping objects, and others. These problems complicate the process of object detection and lead to errors in image processing algorithms. To solve these problems, we study the advantages, disadvantages, and technical features of models and methods for detecting objects in digital images for their reasonable selection in practical applications. Figs. 2. Refs. 11 titles.

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Zhu, Chunhua, Jiarui Liang, and Fei Zhou. "Transfer Learning-Based YOLOv3 Model for Road Dense Object Detection." Information 14, no. 10 (2023): 560. http://dx.doi.org/10.3390/info14100560.

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Stemming from the overlap of objects and undertraining due to few samples, road dense object detection is confronted with poor object identification performance and the inability to recognize edge objects. Based on this, one transfer learning-based YOLOv3 approach for identifying dense objects on the road has been proposed. Firstly, the Darknet-53 network structure is adopted to obtain a pre-trained YOLOv3 model. Then, the transfer training is introduced as the output layer for the special dataset of 2000 images containing vehicles. In the proposed model, one random function is adapted to init

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Li, Yong, Guofeng Tong, Huashuai Gao, Yuebin Wang, Liqiang Zhang, and Huairong Chen. "Pano-RSOD: A Dataset and Benchmark for Panoramic Road Scene Object Detection." Electronics 8, no. 3 (2019): 329. http://dx.doi.org/10.3390/electronics8030329.

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Panoramic images have a wide range of applications in many fields with their ability to perceive all-round information. Object detection based on panoramic images has certain advantages in terms of environment perception due to the characteristics of panoramic images, e.g., lager perspective. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. Their performance depends on the large amount of training data. Therefore, a good training dataset is a prerequisite for the methods to achieve better recognition results. Then, we constru

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Suherman, Endang, Ben Rahman, Djarot Hindarto, and Handri Santoso. "Implementation of ResNet-50 on End-to-End Object Detection (DETR) on Objects." SinkrOn 8, no. 2 (2023): 1085–96. http://dx.doi.org/10.33395/sinkron.v8i2.12378.

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Object recognition in images is one of the problems that continues to be faced in the world of computer vision. Various approaches have been developed to address this problem, and end-to-end object detection is one relatively new approach. End-to-end object detection involves using the CNN and Transformer architectures to learn object information directly from the image and can produce very good results in object detection. In this research, we implemented ResNet-50 in an End-to-End Object Detection system to improve object detection performance in images. ResNet-50 is a CNN architecture that

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Murthy, Chinthakindi Balaram, Mohammad Farukh Hashmi, Neeraj Dhanraj Bokde, and Zong Woo Geem. "Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review." Applied Sciences 10, no. 9 (2020): 3280. http://dx.doi.org/10.3390/app10093280.

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In recent years there has been remarkable progress in one computer vision application area: object detection. One of the most challenging and fundamental problems in object detection is locating a specific object from the multiple objects present in a scene. Earlier traditional detection methods were used for detecting the objects with the introduction of convolutional neural networks. From 2012 onward, deep learning-based techniques were used for feature extraction, and that led to remarkable breakthroughs in this area. This paper shows a detailed survey on recent advancements and achievement

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Takyudin, Takyudin, Iskandar Fitri, and Yuhandri Yuhandri. "Catfish Fry Detection and Counting Using YOLO Algorithm." Journal of Applied Informatics and Computing 7, no. 2 (2023): 192–97. http://dx.doi.org/10.30871/jaic.v7i2.6746.

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The development of computer vision technology is growing very fast and penetrating all sectors, including fisheries. This research focuses on detecting and counting catfish fry. This research aims to apply deep learning in detecting catfish fry objects and counting accurately so as to help farmers and buyers reduce the risk of loss. The detection system in this research uses digital image processing techniques as a way to obtain information from the detection object. The research method uses YOLO Object Detection which has a very fast ability to identify objects. The object detected is a catfi

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Bandi, Sudheer Reddy, G. Merlin Linda, S. Nagarjuna Chary, and K. Spurthy. "Vehicle Detection in SAR Satellite Images Using Yolov8 Oriented Bounding Box Detection Algorithm." Indian Journal Of Science And Technology 18, Sp1 (2025): 62–71. https://doi.org/10.17485/ijst/v18si1.icamada38.

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Objectives: To detect object localization in visualizing Synthetic Aperture Radar, known as SAR Satellite Images, by introducing an additional angle to rotate and fit the orientation of the object placement, which is more precise, essential for investigating vehicle targets in a wide area of remote sensing technology. Methods: The orientation of the object detection is carried out on Ka, Ku, and X bands of data by exhibiting richness, stability, and challenge as fundamental properties. The SAR Image data for the VEhicle Detection (SIVED) dataset involves complex background information with 104

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Zhu, Yicheng, Zhenhua Ai, Jinqiang Yan, Silong Li, Guowei Yang, and Teng Yu. "NATCA YOLO-Based Small Object Detection for Aerial Images." Information 15, no. 7 (2024): 414. http://dx.doi.org/10.3390/info15070414.

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The object detection model in UAV aerial image scenes faces challenges such as significant scale changes of certain objects and the presence of complex backgrounds. This paper aims to address the detection of small objects in aerial images using NATCA (neighborhood attention Transformer coordinate attention) YOLO. Specifically, the feature extraction network incorporates a neighborhood attention transformer (NAT) into the last layer to capture global context information and extract diverse features. Additionally, the feature fusion network (Neck) incorporates a coordinate attention (CA) module

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M, Rajendra, Rajesh Y A, Rahul Balaji S, Puneeth Kumar R, and Shashikala N. "Waste and Vehicle Detection Using YOLO." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3587–90. http://dx.doi.org/10.22214/ijraset.2022.43124.

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Abstract: Automatic detection and classification of objects is an important functionality of image analysis. Due to the nature and size of objects and the varied visual features, it becomes challenging to detect and classify objects in aerial images. Manual detection of objects in these images is very time consuming due to the nature and that data captured in these images. It is desirable to automate the detection of various features or objects from these images. The conventional methods for object classification involve two stages: (i) Identify the regions with object presence in the image an

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Volkov, Vladimir. "Adaptive multi-threshold object selection in remote sensing images." Information and Control Systems, no. 3 (June 15, 2020): 12–24. http://dx.doi.org/10.31799/1684-8853-2020-3-12-24.

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Introduction: Detection, selection and analysis of objects of interest in digital images is a major problem for remote sensing and technical vision systems. The known methods of threshold detection and selection of objects avoid using the processing results, therefore not providing a low probability of false alarms, and not keeping the shape of the selected objects well enough. There are only few results from the studies about quantifying the quality of such algorithms on either model or real images. Purpose: Studying the effectiveness of algorithms for detecting, selecting, and localizing obj

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Meng, Lu, Lijun Zhou, and Yangqian Liu. "SODCNN: A Convolutional Neural Network Model for Small Object Detection in Drone-Captured Images." Drones 7, no. 10 (2023): 615. http://dx.doi.org/10.3390/drones7100615.

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Drone images contain a large number of small, dense targets. And they are vital for agriculture, security, monitoring, and more. However, detecting small objects remains an unsolved challenge, as they occupy a small proportion of the image and have less distinct features. Conventional object detection algorithms fail to produce satisfactory results for small objects. To address this issue, an improved algorithm for small object detection is proposed by modifying the YOLOv7 network structure. Firstly, redundant detection head for large objects is removed, and the feature extraction for small ob

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Garad, Nakshatra. "Smart Video Surveillance Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 5392–96. http://dx.doi.org/10.22214/ijraset.2024.60441.

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Abstract: There is a lot of assessment happening in the business about video observation among them; the occupation of CCTV accounts has been blocked. CCTV cameras are put all around the spots for perception and security. In today's digital age, the increasing availability of data and advancements in computer vision have paved the way for numerous applications of object detection. This powerful technology has found its significance in various domains, including video surveillance and image retrieval systems. Object detection enables machines to identify and locate objects within images or vide

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Crawford, Eric, and Joelle Pineau. "Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3412–20. http://dx.doi.org/10.1609/aaai.v33i01.33013412.

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There are many reasons to expect an ability to reason in terms of objects to be a crucial skill for any generally intelligent agent. Indeed, recent machine learning literature is replete with examples of the benefits of object-like representations: generalization, transfer to new tasks, and interpretability, among others. However, in order to reason in terms of objects, agents need a way of discovering and detecting objects in the visual world - a task which we call unsupervised object detection. This task has received significantly less attention in the literature than its supervised counterp

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Xiao, Zhifeng, Linjun Qian, Weiping Shao, Xiaowei Tan, and Kai Wang. "Axis Learning for Orientated Objects Detection in Aerial Images." Remote Sensing 12, no. 6 (2020): 908. http://dx.doi.org/10.3390/rs12060908.

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Orientated object detection in aerial images is still a challenging task due to the bird’s eye view and the various scales and arbitrary angles of objects in aerial images. Most current methods for orientated object detection are anchor-based, which require considerable pre-defined anchors and are time consuming. In this article, we propose a new one-stage anchor-free method to detect orientated objects in per-pixel prediction fashion with less computational complexity. Arbitrary orientated objects are detected by predicting the axis of the object, which is the line connecting the head and tai

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Sharba, Ahmed, and Hussain Kanaan. "Improving Tiny Object Detection in Aerial Images with Yolov5." Journal of Engineering and Sustainable Development 29, no. 1 (2025): 57–67. https://doi.org/10.31272/jeasd.2682.

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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. However, such images often lack semantic detail and struggle to identify small, densely packed objects accurately. This paper proposes improvements to the You Only Look Once version 5 (YOLOv5) model to enhance small object detection. Key modifications include adding a new prediction head with a 160×160 feature map, replacing the Sigmoid Linear Unit (SiLU) activation function with the Exponential Linear Unit (E

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Li, Hongshan. "Survey of object detection in terahertz images." Applied and Computational Engineering 19, no. 1 (2023): 103–8. http://dx.doi.org/10.54254/2755-2721/19/20231016.

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In recent years, object detection algorithms have undergone a further development and improvement, resulting in a wider range of application scenarios. As one of the most fundamental and challenging issues in the field of computer vision, the application of object detection in the field of security has also received considerable attention. Terahertz (THz) imaging which is widely used in this area because of the ability to detect hidden objects, as a type of electromagnetic wave imaging with poor imaging performance and low resolution, traditional target detection methods cannot achieve high ro

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Shin, Yeonha, Heesub Shin, Jaewoo Ok, Minyoung Back, Jaehyuk Youn, and Sungho Kim. "DCEF2-YOLO: Aerial Detection YOLO with Deformable Convolution–Efficient Feature Fusion for Small Target Detection." Remote Sensing 16, no. 6 (2024): 1071. http://dx.doi.org/10.3390/rs16061071.

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Deep learning technology for real-time small object detection in aerial images can be used in various industrial environments such as real-time traffic surveillance and military reconnaissance. However, detecting small objects with few pixels and low resolution remains a challenging problem that requires performance improvement. To improve the performance of small object detection, we propose DCEF 2-YOLO. Our proposed method enables efficient real-time small object detection by using a deformable convolution (DFConv) module and an efficient feature fusion structure to maximize the use of the i

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Guan, Yurong, Muhammad Aamir, Zhihua Hu, et al. "A Region-Based Efficient Network for Accurate Object Detection." Traitement du Signal 38, no. 2 (2021): 481–94. http://dx.doi.org/10.18280/ts.380228.

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Object detection in images is an important task in image processing and computer vision. Many approaches are available for object detection. For example, there are numerous algorithms for object positioning and classification in images. However, the current methods perform poorly and lack experimental verification. Thus, it is a fascinating and challenging issue to position and classify image objects. Drawing on the recent advances in image object detection, this paper develops a region-baed efficient network for accurate object detection in images. To improve the overall detection performance

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Chen, Y., L. Pang, H. Liu, and X. Xu. "WAVELET FUSION FOR CONCEALED OBJECT DETECTION USING PASSIVE MILLIMETER WAVE SEQUENCE IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 193–98. http://dx.doi.org/10.5194/isprs-archives-xlii-3-193-2018.

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PMMW imaging system can create interpretable imagery on the objects concealed under clothing, which gives the great advantage to the security check system. Paper addresses wavelet fusion to detect concealed objects using passive millimeter wave (PMMW) sequence images. According to PMMW real-time imager acquired image characteristics and storage methods，firstly, using the sum of squared difference (SSD) as the image-related parameters to screen the sequence images. Secondly, the selected images are optimized using wavelet fusion algorithm. Finally, the concealed objects are detected by mean fil

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Coulson, Andrew V., W. Hoyt Thomas, and Caixia Wang. "A Comparative Study of Deep Learning-Based Models for Object Detection in Remote Sensing Imagery." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-M-5-2024 (March 17, 2025): 201–5. https://doi.org/10.5194/isprs-archives-xlviii-m-5-2024-201-2025.

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Abstract. Object detection contributes significantly to advancing image interpretation and understanding. The advent of deep learning-based methods has significantly advanced this field. However, the distinctive characteristics of remote sensing images, including large directional variations, scale differences, and complex and cluttered backgrounds, pose considerable challenges for accurate target detection. In this work, we compare the detection accuracy and processing speed of several state-of-the-art models by detecting palm trees in optical satellite imagery. This work aims to explore how

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Wei, Linhai, Chen Zheng, and Yijun Hu. "Oriented Object Detection in Aerial Images Based on the Scaled Smooth L1 Loss Function." Remote Sensing 15, no. 5 (2023): 1350. http://dx.doi.org/10.3390/rs15051350.

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Although many state-of-the-art object detectors have been developed, detecting small and densely packed objects with complicated orientations in remote sensing aerial images remains challenging. For object detection in remote sensing aerial images, different scales, sizes, appearances, and orientations of objects from different categories could most likely enlarge the variance in the detection error. Undoubtedly, the variance in the detection error should have a non-negligible impact on the detection performance. Motivated by the above consideration, in this paper, we tackled this issue, so th

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