摘要：The number of end-of-life mobile phones is increasing every year, which includes parts that have high reuse values and various dangerous and toxic compounds. An intellectualized and automatic upgrade of the disassembly process of the end-of-life mobile phones would enhance the recycling value as well as efficiency. It would reduce the pollution in the environment. The detection of end-of-life mobile phone parts plays a critical role in automatic disassembly and recycling. This study offers an image processing-based approach for identifying important parts of mobile phones that are nearing the end of their useful lives. An image enhancement approach has been utilized for generating disassembly datasets of end-of-life mobile phones from several brands and models, and different retirement states. The YOLOv5m detection model is applied to train as well as validate the detection model on the customized datasets. According to the results, the proposed approach allows the intelligent detection of battery, camera, mainboard and screw. In the validation set, the Precision, Recall and [email protected] are 99.4%, 98.4% and 99.3%, respectively. Additionally, several path planning algorithms are utilized for the disassembly plan of screws which indicates that the genetic algorithm’s use increases the efficiency of disassembly.
