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An Image Analysis of Coastal Debris Detection -Detection of microplastics using deep learning-
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Researchers developed a deep learning-based coastal debris detection system using YOLOv7 and the SAHI vision library to identify microplastics in image data collected from shorelines. The system demonstrated effective detection performance and offers a scalable approach for automated monitoring of microplastic litter in coastal environments.
To address the issue of litter drifting ashore, this study developed a deep learning-based microplastic detection system.The system employed yolov7 [1] as its deep learning network, complemented by SAHI (Slicing Aided Hyper Inference) [2] as an additional vision library.yolov7 is renowned for its efficacy in real-time object detection.Our experimental framework involved four tests, utilizing two variations of yolov7 -the standard model and yolov7-e6e -in conjunction with SAHI.The effectiveness of each test was quantified using metrics such as Intersection over Union (IoU), Precision, Recall, F-measure, and Detection Time in seconds.For our dataset, we gathered images from actual cleanup locations, such as Hokuto Mizukumi Park.The model's discriminator underwent 700 training iterations, with a learning rate set at 0.001.Experimental results showed that it detects fairly small microplastics.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.