Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Department of Electrical and Electronic Engineering, Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia (USIM), Negeri Sembilan, Malaysia.
Front Public Health. 2022 Aug 12;10:907280. doi: 10.3389/fpubh.2022.907280. eCollection 2022.
Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers.
由于城市化的发展，固体废物污染日益成为河流面临的一个问题，可能会威胁到人类健康、生态完整性和生态系统服务。城市景观中的河流管理需要采用最佳管理实践，因为河流是城市生态文明的重要组成部分，必须协调城市发展和河流保护之间的联系。因此，实施适当和创新的措施对于控制河流中的垃圾污染至关重要。能够自主清理废物的机器人可能是有效管理河流污染的一个好方法。识别和获取垃圾的精确位置是清洁机器人视觉系统中最关键的部分。计算机视觉为计算机理解和解释周围物体铺平了道路。开发一个准确的计算机视觉系统是机器人平台开发的关键步骤，因为这是后续操作和抓取系统之前的前端观察系统。这项工作的范围是获取河流上漂浮垃圾的视觉信息，这对于构建河流清洁机器人的机器人平台至关重要。在本文中，开发了一种基于改进型 You Only Look Once (YOLO) 模型的自动检测系统，用于在各种条件下检测漂浮垃圾，例如波动的光照、复杂的背景和遮挡。所提出的目标检测模型已被证明可以促进快速收敛，从而缩短训练时间。此外，所提出的目标检测模型已被证明可以通过加强非线性特征提取过程来提高检测精度。结果表明，所提出的模型的平均精度 (mAP) 值达到了 89%。因此，所提出的模型被认为可以识别五类垃圾，如塑料瓶、铝罐、塑料袋、泡沫塑料和塑料容器。
