Applying Multi-YOLO for Enhanced Product and Fire Detection in Image Analysis
Keywords:
fire detection, product recognition, deep learning, Multi-YOLO, decision fusionAbstract
With its wide range of uses and intense research interest, computer vision presents a challenging problem when it comes to product and fire detection in images. In addition to providing useful applications including improving consumer product information, enabling image-based rapid payments, automating product availability management, and building early fire warning systems, this task entails identifying goods and fire in photographs with diverse backdrops. However, there is a problem with the widely held belief in product detection research, which holds that training data should reflect actual situations. The effectiveness of product detection systems is impacted by the fact that testing data obtained in a variety of contexts does not match training data, which is frequently gathered under perfect conditions. This work presents a deep learning method for image-based product detection in response to these difficulties. To identify products in photos, the suggested model, known as Multi-YOLO, makes use of several YOLO models. Every element operates as a separate YOLO model, and Fusion rules combine their outputs to create a single output. The experimental results show how well the suggested model works, especially when applied to our collection of product photos, and emphasize its potential for reliable product detection in practical settings. Furthermore, the study's integration of the Multi-YOLO model within a comprehensive early fire alert system paves the way for enhanced fire prevention strategies and improved public safety outcomes.
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