Comparison of YOLO Versions for Object Detection from Aerial Images

Atik, Muhammed Enes; Duran, Zaide; ÖZGÜNLÜK, Roni

doi:10.30897/ijegeo.1010741

Cited by 25 publications

(12 citation statements)

References 28 publications

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“…The AP value is equal to the size of the area under this curve. This formula sums the horizontal slices for each of the threshold values specified to calculate this area [23]. In this article, we discussed heatmap visualization to better understand and analyze customer behavior and movements in retail stores.…”

Section: Resultsmentioning

confidence: 99%

Heatmap creation with YOLO-Deep SORT system customized for in-store customer behavior analysis

Şimşek,

Tekbaş

2024

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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Due to the limitations of the hardware system, analysis of retail stores has caused problems such as excessive workload, incomplete analysis, slow analysis speed, difficult data collection, non-real-time data collection, passenger flow statistics, and density analysis. However, heatmaps are a viable solution to these problems and provide adaptable and effective analysis. In this paper, we propose to use the deep sequence tracking algorithm together with the YOLO object recognition algorithm to create heatmap visualizations. We will present key innovations of our customized YOLO-Deep SORT system to solve some fundamental problems in in-store customer behavior analysis. These innovations include our use of footpad targeting to make bounding boxes more precise and less noisy. Finally, we made a comprehensive evaluation and comparison to determine the success rate of our system and found that the success rate was higher than the systems we compared in the literature. The results show that our heatmap visualization enables accurate, timely, and detailed analysis.

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Section: Resultsmentioning

confidence: 99%

Heatmap creation with YOLO-Deep SORT system customized for in-store customer behavior analysis

Şimşek,

Tekbaş

2024

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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“…In order to decrease training time and produce a more generalized network, YOLO9000 (Redmon and Farhadi, 2017) has been proposed with some modifications to original YOLO such as batch normalization, higher resolution classifier and anchor boxes with convolutions (Atik et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

An Assessment of YOLO Architectures for Oil Tank Detection from SPOT Imagery

Bakırman

2023

International Journal of Environment and Geoinformatics

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Since it can be used to manage and estimate oil reserves, the inventory of oil tanks is essential for both the economy and the military applications. Considering oil tanks contain valuable materials required for transportation and industrial production, they are a significant type of target. Oil tank detection techniques have several uses, including monitoring disasters, preventing oil leaks, designing cities, and assessing damage. Huge amount of satellite imagery has recently been available and it is used in both the military and civil applications. The new spaceborne sensors' higher resolution enables the detection of targeted objects. Therefore, remote sensing instruments provide ideal tools for oil tank detection task. Conventional approaches for oil tank detection from high resolution remote sensing imagery generally relies on geometric shape, structure, contract differences and color information of the boundary or hand-crafted features. However, these methods come along with vulnerabilities and hence it can be challenging to obtain accurate detection in the presence of a number of disturbance elements, particularly a wide range of colours, size variations, and the shadows that view angle and illumination create. Therefore, deep learning-based methods can provide a big advantage for solution of this task. In this regard, this study employs four YOLO models namely YOLOv5, YOLOX, YOLOv6 and YOLOv7 for oil tank detection from high-resolution optical imagery. Our results show that YOLOv7 and YOLOv5 architectures provide more accurate detections with mean average precision values of 68.11% and 69.69%, respectively. The experiments and visual inspections reveal efficiency, generalization and transferability of these models.

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“…With the developing technology, the usage of deep learning approaches in object detection studies has intensified. Studies have been published on not only the extraction of building façade elements, but also the detection of all kinds of objects through the image (Cepni et al, 2020;Atik et al, 2022;Atik and Ipbuker, 2020). There are both detection and segmentation studies for building façade elements.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning-Based Door and Window Detection From Building Façade

Sezen

Çakır

Atik

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Detecting building façade elements is a crucial problem in computer vision for image interpretation. In Building Information Modeling (BIM) studies, the detection of building façade elements has an important role. BIM is a tool that allows maintaining a digital representation of all aspects of building information; therefore, it will enable the storage of almost any data related to a given structure, regarding its geometric and non-geometric aspects. Façade segmentation was first studied in the 1970s using hand-crafted expertise. Later, detection and segmentation studies emerged based on shapes of objects and parametric rules. With the developing technology, deep learning approaches in object detection studies have intensified. It is obvious that the desired analyses can be performed faster with deep learning approaches. However, deep learning methods require large training data. Algorithms that consider different situations and are suitable for real-world scenarios continue to be developed. The need in this direction continues in the literature. In this study, door and window detection was carried out with deep learning on an original data set. The algorithms used are YOLOv3, YOLOv4, YOLOv5, and Faster R-CNN. Precision, recall and mean average precision (mAP) are used as evaluation metrics. As a result of the study, precision, recall, and mAP values with YOLOv5 were obtained as 0.85, 0.72, and 0.79, respectively. With Faster R-CNN with the lowest performance, precision, recall, and mAP were obtained as 0.54, 0.63, and 0.54, respectively.

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Comparison of YOLO Versions for Object Detection from Aerial Images

Cited by 25 publications

References 28 publications

Heatmap creation with YOLO-Deep SORT system customized for in-store customer behavior analysis

Heatmap creation with YOLO-Deep SORT system customized for in-store customer behavior analysis

An Assessment of YOLO Architectures for Oil Tank Detection from SPOT Imagery

Deep Learning-Based Door and Window Detection From Building Façade

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