A Deep Learning Approach to Plastic Bottle Waste Detection on the Water Surface using YOLOv6 and YOLOv7
Received: 28 September 2024 | Revised: 8 October 2024, 17 October 2024, and 28 October 2024 | Accepted: 29 October 2024 | Online: 2 December 2024
Corresponding author: Diva Kurnianingtyas
Abstract
Deep learning is a branch of machine learning with many layers, such as the You Only Look Once (YOLO) method. From various versions of YOLO, YOLOv6 and YOLOv7 are considered more prominent because they achieve high Mean Average Precision (mAP) values. Both versions of YOLO have been implemented into various problems, especially in the waste detection problem. Plastic bottle waste is one of the most common types of waste that pollutes Indonesian waters. This study aims to solve this problem by helping to sort waste in surface waters by applying YOLOv6 and YOLOv7. FloW-Img was used, obtained on request from the Orcaboat website. The dataset consists of 500,000 bottle objects in 2,000 images. The YOLOv6 and YOLOv7 models were evaluated using mAP and running time. The results show that YOLOv6 and YOLOv7 can handle bottle waste detection well, with mAP values of 0.873 and 0.512, respectively. In addition, YOLOv6 (4.21 m/s) has a higher detection speed than YOLOv7 (13.7 m/s). However, in tests with images that do not have bottle objects, YOLOv7 provides better detection accuracy and consistency results, making it more suitable for real-world applications that demand high accuracy in environments with much visual noise.
Keywords:
artificial intelligence, neural networks, computer vision, optimization, waste detectionDownloads
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