Object Detection for Low Light Images

Mpouziotas, Dimitrios; Mastrapas, Eleftherios; Dimokas, Nikos; Karvelis, Petros; Glavas, Euripidis

doi:10.1109/seeda-cecnsm57760.2022.9932921

Cited by 5 publications

(2 citation statements)

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“…The pre-processing layer works by dynamically loading the models based on their individual framework. Additionally, each frame is enhanced with the use of the gamma correction technique [29], to reduce sunlight degradation. The footage is loaded in the form of a dataset.…”

Section: Layer One: Pre-processing Layermentioning

confidence: 99%

Advanced Computer Vision Methods for Tracking Wild Birds from Drone Footage

Mpouziotas,

Karvelis,

Stylios

2024

Drones

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Wildlife conservationists have historically depended on manual methods for the identification and tracking of avian species, to monitor population dynamics and discern potential threats. Nonetheless, many of these techniques present inherent challenges and time constraints. With the advancement in computer vision techniques, automated bird detection and recognition have become possible. This study aimed to further advance the task of detecting wild birds using computer vision methods with drone footage, as well as entirely automating the process of detection and tracking. However, detecting objects from drone footage presents a significant challenge, due to the elevated altitudes, as well as the dynamic movement of both the drone and the birds. In this study, we developed and introduce a state-of-the-art model titled ORACLE (optimized rigorous advanced cutting-edge model for leveraging protection to ecosystems). ORACLE aims to facilitate robust communication across multiple models, with the goal of data retrieval, rigorously using various computer vision techniques such as object detection and multi-object tracking (MOT). The results of ORACLE’s vision models were evaluated at 91.89% mAP at 50% IoU.

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Section: Layer One: Pre-processing Layermentioning

confidence: 99%

Advanced Computer Vision Methods for Tracking Wild Birds from Drone Footage

Mpouziotas,

Karvelis,

Stylios

2024

Drones

Self Cite

View full text Add to dashboard Cite

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“…This section describes the method we used to enhance the samples in our dataset, in order to improve the overall mean Average Precision (mAP) of our model's results [20,21]. To address the degradation and lack of visibility in certain regions of the image caused by the Sun, we applied gamma correction [22] to enhance our dataset.…”

Section: Image Enhancementmentioning

confidence: 99%

Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques

et al. 2023

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Wildlife conservationists have traditionally relied on manual identification and tracking of bird species to monitor populations and identify potential threats. However, many of these techniques may prove to be time-consuming. With the advancement of computer vision techniques, automated bird detection and recognition have become possible. In this manuscript, we present an application of an object-detection model for identifying and tracking wild bird species in natural environments. We used a dataset of bird images captured in the wild and trained the YOLOv4 model to detect bird species with high accuracy. We evaluated the model’s performance on a separate set of test images and achieved an average precision of 91.28%. Our method surpassed the time-consuming nature of manual identification and tracking, allowing for efficient and precise monitoring of bird populations. Through extensive evaluation on a separate set of test images, we demonstrated the performance of our model. Furthermore, our results demonstrated the potential of using YOLOv4 for automated bird detection and monitoring in the wild, which could help conservationists better understand bird populations and identify potential threats.

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Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation

Shovo,

Abir,

Kabir

et al. 2024

Cognitive Comp and Systems

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Low‐light object detection is needed for ensuring security, enabling surveillance, and enhancing safety in diverse applications, including autonomous vehicles, surveillance systems, and search and rescue operations. A comprehensive study on low‐light object detection is presented using state‐of‐the‐art you only look once (YOLO) models, including YOLOv3, YOLOv5, YOLOv6, and YOLOv8, aiming to enhance detection performance under challenging low‐light conditions. The ExDark dataset is a dataset that consists of adequate low‐light images, modified to simulate realistic low‐light scenarios, and employed for evaluation. The deep learning algorithm optimises YOLO's architecture for low‐light detection by adapting the network structure and training strategies while preserving the algorithm's integrity. The experimental results show that YOLOv8 consistently outperforms baseline models, achieving significant improvements in accuracy and robustness in low‐light scenarios. The deep learning algorithm that acquired the best score, YOLOv8s, had a mean average precision score of 0.5513. This work contributes to the field of low‐light object detection, offering promising solutions for real‐world applications like nighttime surveillance and autonomous navigation in low‐light conditions, addressing the growing demand for advanced low‐light object detection.

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Object Detection for Low Light Images

Cited by 5 publications

References 0 publications

Advanced Computer Vision Methods for Tracking Wild Birds from Drone Footage

Advanced Computer Vision Methods for Tracking Wild Birds from Drone Footage

Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques

Advancing low‐light object detection with you only look once models: An empirical study and performance evaluation

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