树莓皮
水下
环境科学
塑料废料
海洋工程
工程类
汽车工程
计算机科学
嵌入式系统
地质学
废物管理
物联网
海洋学
作者
M Delina,Taryudi Taryudi,Muhammad Amin,Aarij Mahmood Hussaan,Trismidianto,Fatahul Arifin,S M I Syah
出处
期刊:Journal of physics
[IOP Publishing]
日期:2024-10-01
卷期号:2866 (1): 012048-012048
标识
DOI:10.1088/1742-6596/2866/1/012048
摘要
Abstract Underwater Plastic waste has had a serious impact on the environment. Unfortunately, Detecting and collecting underwater plastic waste is still a challenge, especially in Indonesia because of its position in the water, and the water turbidity. In previous research, a computer algorithm for detecting underwater plastic waste has been developed with Yolov3. To improve the research, we developed a Remotely Operated Underwater Vehicle (ROUV) with Raspberry Pi through the Research and Development (R&D) method. This ROUV is like a submarine that operates in the water and is integrated with a camera and computer algorithm to detect plastic waste. To get close to the real condition, we created an artificial water environment in a pond with various turbidity (from 20 to 200 Nephelometric Turbidity Unit). We use 30 pieces of plastic waste in several colors: white, red, black, and transparent. The plastic waste dipped in the water and was then detected by the ROUV camera. The result shows that the effective threshold for object detection is around 100 Nephelometric Turbidity Unit (NTU). Below 100 NTU, although the number of detected plastic images decreases as the turbidity increases, the visibility and contrast still allow for reasonably good object detection. The average confidence value for detection at turbidity levels below 100 NTU is 77%. At high turbidity conditions, the detection model may require adjustment or retraining with data that reflects those turbidity conditions.
科研通智能强力驱动
Strongly Powered by AbleSci AI