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.

No abstract

Marine litter has been one of the major challenges and a well-known issue across the globe for decades. 6.4 million tonnes of marine debris per year is estimated to enter water environments, with 8 million items entering each day. These statistics are so worrying, and mitigation steps need to be taken for the sake of a sustainable community. The major contributor to marine litter is no other than riverine litter. However, when there is not enough data about the amount of litter being transported, making quantitative data for monitoring impossible. Nowadays, most countries still use visual counting, which limits the feasibility of scaling to long-term monitoring at multiple locations. Therefore, an object detector using one of the deep learning algorithms, You Only Look Once version 4 (YOLOv4), is developed for floating debris of riverine monitoring system to mitigate the problem mentioned earlier. The proposed automated detection method has the capability to detect and categorize riverine litter, which can be improved in terms of detection speed and accuracy using YOLOv4. The detector is trained on five object classes such as styrofoam, plastic bags, plastic bottle, aluminium can and plastic container. Image augmentation technique is implemented into the previous datasets to increase training and validation datasets, which results in the increase of accuracy of the training. Some YOLOv4 and YOLOv4-tiny parameters have also been studied and manipulated to see their effects on the training.