Benchmarking YOLOv5 models for improved human detection in search and rescue missions

Bachir, Namat; Memon, Qurban Ali

doi:10.1016/j.jnlest.2024.100243

Cited by 4 publications

(1 citation statement)

References 11 publications

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“…YOLOv5 streamlines the design by implementing a single-stage technique that eliminates the need of anchor boxes. YOLOv5 enhances efficiency by implementing a simplified structure and employing optimization methods such as Cross Stage Partial Network (CSP) Darknet53 and adaptive scaling [17]. This makes it more suitable for real-time applications and deployment on devices with limited resources.…”

Section: Object Detectionmentioning

confidence: 99%

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Maity,

Raja Sankari,

et al. 2024

Biomed. Phys. Eng. Express

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Presently, close to two million patients globally succumb to gastrointestinal reflux diseases (GERD). Video endoscopy represents cutting-edge technology in medical imaging, facilitating the diagnosis of various gastrointestinal ailments including stomach ulcers, bleeding, and polyps. However, the abundance of images produced by medical video endoscopy necessitates significant time for doctors to analyze them thoroughly, posing a challenge for manual diagnosis. This challenge has spurred research into computer-aided techniques aimed at diagnosing the plethora of generated images swiftly and accurately. The novelty of the proposed methodology lies in the development of a system tailored for the diagnosis of gastrointestinal diseases. The proposed work used an object detection method called Yolov5 for identifying abnormal region of interest and Deep LabV3+ for segmentation of abnormal regions in GERD. Further, the features are extracted from the segmented image and given as an input to the seven different machine learning classifiers and custom deep neural network model for multi-stage classification of GERD. The DeepLabV3+ attains an excellent segmentation accuracy of 95.2% and an F1 score of 93.3%. The custom dense neural network obtained a classification accuracy of 90.5%. Among the seven different machine learning classifiers, support vector machine (SVM) outperformed with classification accuracy of 87% compared to all other class outperformed combination of object detection, deep learning-based segmentation and machine learning classification enables the timely identification and surveillance of problems associated with GERD for healthcare providers.

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Section: Object Detectionmentioning

confidence: 99%

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Maity,

Raja Sankari,

et al. 2024

Biomed. Phys. Eng. Express

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Human Detection in Images Captured by UAVs in Natural Environments

De Miguel,

Caubilla,

Cambra

et al. 2024

Lecture Notes in Networks and Systems

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Advanced Customer Behavior Tracking and Heatmap Analysis with YOLOv5 and DeepSORT in Retail Environment

Shili,

Jayasingh,

Hammedi

2024

Electronics

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This paper presents a computer-vision-based approach designed to enhance product placement and sales strategies in physical retail stores through real-time analysis of customer behavior. Our method employs DeepSORT for tracking and YOLOv5 for object identification to generate heatmaps that illustrate consumer movement patterns and engagement levels across various retail locations. To precisely track customer paths, the procedure starts with the collection of video material, which is then analyzed. Customer interaction and traffic patterns across various retail zones are represented using heatmap visualization, which offers useful information about consumer preferences and product popularity. In order to maximize customer engagement and optimize the shopping experience, businesses may use the findings of this analysis to improve product placements, store layouts, and marketing strategies. With its low intervention requirements and scalable and non-intrusive solution, this system may be used in a variety of retail environments. This system offers a scalable and non-intrusive solution that requires minimal intervention, making it adaptable across different retail settings. Our findings demonstrate the approach’s effectiveness in identifying strategic areas for improvement and adapting retail environments based on real-time customer interaction data. This study underscores the potential of computer vision in retail analytics, enabling data-driven decisions that enhance both customer satisfaction and operational efficiency. This approach gives merchants useful data to develop more responsive, customized, and effective shopping experiences by providing a dynamic perspective of consumer behavior. Retailers may promote a modernized and customer-centered retail management strategy by using this creative application of computer vision to match marketing tactics and shop design with real consumer behaviors.

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Benchmarking YOLOv5 models for improved human detection in search and rescue missions

Cited by 4 publications

References 11 publications

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Explainable AI based automated segmentation and multi-stage classification of gastroesophageal reflux using machine learning techniques

Human Detection in Images Captured by UAVs in Natural Environments

Advanced Customer Behavior Tracking and Heatmap Analysis with YOLOv5 and DeepSORT in Retail Environment

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