Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Filters

2024 - 20253

Settings

Has files: NoSubject: search.filters.subject.YOLOv5

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Optimizing Object Detection in YOLOv5 using Adaptive Genetic Algorithm: A Study on Population Sizes for Enhanced Accuracy
    (Institute of Electrical and Electronics Engineers Inc., 2024) Naik, P.M.; Rudra, B.
    In recent years, deep learning-based object detection models like YOLOv5 have demonstrated remarkable efficiency in accurately identifying objects within images. However, achieving optimal performance still presents challenges. Many researchers have employed hybrid models to enhance the mean average precision (mAP) in the YOLOv5 architecture. However, only a few have explored hyperparameter-based optimization techniques to improve mAP. We use an adaptive genetic algorithm-based approach for analyzing Arecanut X-ray images to improve detection accuracy within the YOLOv5 framework. Balancing population size and computational efficiency is crucial for effective exploration and exploitation of the solution space in genetic algorithms without incurring unnecessary computational costs. This research investigates the influence of various population sizes on mAP using a genetic algorithm. Our findings indicate that a population size of 50 is ideal for achieving the best mAP among the explored sizes, when evolved for 300 generations. © 2024 IEEE.
  • No Thumbnail Available
    Item
    Framework for Lightweight Deep Learning Model Using YOLOv5 for Arecanut Grade Assessment
    (Springer, 2024) Naik, P.; Rudra, B.
    Arecanut grading using Machine Learning (ML) approaches can be less ideal in real-world scenarios that require real-time operations due to the need for rapid processing and adaptation to varying conditions. This paper proposes an efficient and lightweight deep learning model for real-time Arecanut detection suitable for industrial applications. We introduce a novel Arecanut image dataset and explore the YOLOv5 architecture to achieve a lightweight and efficient model for both grading and detection. Our investigation reveals that a hybrid model utilizing GhostNet in Darknet-53 convolutions and a Feature Pyramid Network (FPN) in the YOLOv5 neck structure achieves optimal performance. This model achieves the best mean Average Precision (mAP) of 98.85% while maintaining a compact model size of only 1.9 MB. We compared our design with the latest YOLO models, and ours performed better in detecting Arecanuts. This accurate and cost-effective solution empowers Arecanut producers with the ability to identify top-grade Arecanuts, leading to economic benefits and mitigating health concerns associated with manual grading processes. Finally, we propose a framework for integrating this model into the agro-industry for future real-world implementation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.
  • No Thumbnail Available
    Item
    Deep learning-based arecanut detection for X-ray radiography: improving performance and efficiency for automated classification and quality control
    (Taylor and Francis Ltd., 2025) Naik, P.M.; Rudra, B.
    X-ray radiography is a valuable, non-destructive tool and can be used to examine the internal components or quality attributes of agricultural commodities, including arecanut. The true quality of an arecanut can be determined using destructive methods through visual inspection. However, dissected arecanuts do not have a shelf life. There is no non-destructive method available for grading arecanuts. We employ X-ray imaging as an aid to conduct internal examinations of arecanuts, allowing for thorough inspection without causing damage. A custom X-ray image dataset of arecanuts is created for automated interpretation of grades. We developed a hybrid arecanut grading model using YOLOv5s architecture incorporating the Stem, the GhostNet and the Transformer blocks. The proposed hybrid architecture outperforms in comparison with state-of-the-art models with a mean average precision (mAP) of 97.30%. The proposed 9.5 MB lightweight model can easily fit into X-ray devices, making it ideal for detecting arecanut grades in the industry. This method could transform the standards of quality inspection for arecanut. Its incorporation could establish a new industry benchmark for unparalleled quality assessment using X-ray technology as a non-destructive tool. © 2024 Informa UK Limited, trading as Taylor & Francis Group.