An automated solid waste detection using the optimized YOLO model for riverine management - Zailan, Nur Athirah; Azizan, Muhammad Mokhzaini; Hasikin, Khairunnisa; Mohd Khairuddin, Anis Salwa; Khairuddin, Uswah (Frontiers in Public Health, 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 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 at 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 given a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward 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 of floating garbage on the river which is vital in building a robotic platform for river cleaning robot. In this paper, an automated detection system based on 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 shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has shown to improve the detection accuracy by strengthening the nonlinear feature extraction process. The results showed that the proposed model achieved mean Average Precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage such as plastic bottle, aluminum can, plastic bag, styrofoam , and plastic container.