RETRACTED: An efficient object detection using OFSA for thermal imaging

Teju, V.; Bhavana, D.

doi:10.1177/0020720920944434

Cited by 19 publications

(10 citation statements)

References 19 publications

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“…In this section, YOLOv5 and YOLOv8 are compared to previous researchers [1] who used YOLOv4-tiny, Faster-RCNN, SSD-512 and YOLOv4 with the same dataset. Table 2 and Table 3 show the performance results of YOLOv5 and YOLOv8 on the testing dataset.…”

Section: Resultsmentioning

confidence: 99%

“…The use of infrared thermal data in object detection offers several advantages. Firstly, infrared thermal data provides a unique perspective by capturing the thermal energy emitted by objects, allowing for better differentiation and identification of objects in various lighting and weather conditions [1]. Additionally, it enables the detection of objects that are not visible in traditional visual imagery, such as those hidden behind obstacles or in low-light environments.…”

Section: Introductionmentioning

confidence: 99%

“…Infrared thermal data plays a crucial role in object detection, particularly in scenarios where visual light cameras may fall short. One of the key advantages of using infrared thermal images is that they capture information outside the spectrum of the human eye, providing data that is not visible to us [1]. While thermal infrared (TIR) images may not contain the detailed information present in visual RGB images, they still hold value for object detection purposes.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Model for Unmanned Aerial Vehicle-based Object Detection on Thermal Images

Soeleman,

Supriyanto,

Purwanto

2023

RIA

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This research is related to the use of deep learning models on thermal images for object detection using Unmanned Aerial Vehicles (UAVs). Thermal imaging proves to be efficient in environments with minimal light and during nighttime, as it operates based on emitted heat rather than visible light. The ability to detect objects in thermal images enhances surveillance and security measures, particularly in dark conditions. During search and rescue missions, especially in settings with restricted visibility, thermal imaging aids in the identification of individuals or animals by detecting their heat signatures. This facilitates the process of locating them in diverse conditions. Detecting objects at night is challenging due to the lack of illumination. Experiments were conducted using the publicly available HIT-UAV dataset, consisting of 2898 images. This dataset includes several classes such as Person, Car, Bicycle, Other Vehicle, and DontCare. This study proposes the use of both YOLOv5 and YOLOv8 for object detection on this dataset. The YOLOv8 model is the latest model currently available. Both YOLOv5 and YOLOv8 variants were developed by Ultralytics. The experiment used five Yolo models: nano (n), small (s), medium (m), large (l), and extra-large (x). By using YOLOv8m, we achieved a mean Average Precision at IoU threshold 0.5

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning Model for Unmanned Aerial Vehicle-based Object Detection on Thermal Images

Soeleman,

Supriyanto,

Purwanto

2023

RIA

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“…However, such a system’s thermal face images are susceptible to fluctuations in the surrounding temperature, which can result in identification errors. Infrared images work well in all weather and day/night situations to locate people from their surroundings depending on the radiation differential [ 2 , 3 , 4 , 5 ]. The primary concept behind thermal imaging is that each object produces infrared energy uniquely based on its properties and temperature.…”

Section: Introductionmentioning

confidence: 99%

Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net

Singothu

Chandana²

2022

Sensors

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Thermal cameras, as opposed to RBG cameras, work effectively in extremely low illumination situations and can record data outside of the human visual spectrum. For surveillance and security applications, thermal images have several benefits. However, due to the little visual information in thermal images and intrinsic similarity of facial heat maps, completing face identification tasks in the thermal realm is particularly difficult. It can be difficult to attempt identification across modalities, such as when trying to identify a face in thermal images using the ground truth database for the matching visible light domain or vice versa. We proposed a method for detecting objects and actions on thermal human face images, based on the classification of five different features (hat, glasses, rotation, normal, and hat with glasses) in this paper. This model is presented in five steps. To improve the results of feature extraction during the pre-processing step, initially, we resize the images and then convert them to grayscale level using a median filter. In addition, features are extracted from pre-processed images using principle component analysis (PCA). Furthermore, the horse herd optimization algorithm (HOA) is employed for feature selection. Then, to detect the human face in thermal images, the LeNet-5 method is used. It is utilized to detect objects and actions in face areas. Finally, we classify the objects and actions on faces using the ANU-Net approach with the Monarch butterfly optimization (MBO) algorithm to achieve higher classification accuracy. According to experiments using the Terravic Facial Infrared Database, the proposed method outperforms “state-of-the-art” methods for face recognition in thermal images. Additionally, the results for several facial recognition tasks demonstrate good precision.

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“…Gait biometrics is the most popular long-distance biometrics. Human is detected using support vector machine classifier trained on histograms of oriented gradients [1], [2], [3]. Census transform histogram features are extracted to identify humans [4].…”

Section: Introductionmentioning

confidence: 99%

Person Recognition Using Soft Biometric

Srinivasan

2021

Preprint

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Global security concerns raised the multiplication of video surveillance devices. Intelligent systems identify the person captured at the different cameras, angles, views, background, and wearing different accessories. Gait is measured at a distance without human cooperation. In this work, gait recognition is increased by concatenating semantic features with traditional features. Combining features are dimensionally reduced and classified using classifiers. This method motivates in future to increase the gait recognition with less false positive detection.

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RETRACTED: An efficient object detection using OFSA for thermal imaging

Cited by 19 publications

References 19 publications

Deep Learning Model for Unmanned Aerial Vehicle-based Object Detection on Thermal Images

Deep Learning Model for Unmanned Aerial Vehicle-based Object Detection on Thermal Images

Objects and Action Detection of Human Faces through Thermal Images Using ANU-Net

Person Recognition Using Soft Biometric

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