## 1 Introduction  
The importance of maintaining the production of manufacturing systems within the desired boundaries of costs quality and time, while preserving certain levels of flexibility for addressing unplanned situations had a great impact on manufacturing research receiving particular attention [1]. Under this perspective, industrial processes could greatly benefit from the integration of Industry4.0 digital solutions [2] and Artificial Intelligence (AI) technologies [3, 4] that can be used for the management of activities on a factory floor [5]. Among other activities, optimized management of scrap, generated during production, contributes to lean manufacturing, reducing production deadlock situations that may occur due to poor waste management. For example, the lack of space in waste containers may result in unplanned delays in the production process. The main contribution of this work is a computer vision system that uses Deep Learning (DL) and Convolutional Neural Network (CNN) for the automated estimation of the fill level in industrial waste containers of metal scrap. The system has been implemented and deployed in an industrial environment for copper products. This work consists of 5 chapters. Chapter 2 reviews the scientific literature for relevant topics including Industry 4.0, computer vision methods with a focus on machine and deep learning architectures, and solutions and wastebins' fill-level approaches. Chapter 3 presents the main steps of the proposed method. Chapter 4 details the application of the aforementioned method in an industrial case of a copper tube plant. Finally, Chapter 5 discusses the results and presents future directions. The industrial case study demonstrates that the proposed method has sufficient accuracy that allows its practical application in industrial environments for estimating the fill-level of industrial waste containers. The fill-level information can be further exploited for managing scrap on the manufacturing floor. In the metal industries, scrap usually consists of recyclable materials like aluminum, iron, and copper, left over after manufacturing processes. Its value, however, remains high as it can be collected and recycled. A concept for an intelligent waste management system in the copper industry is presented in [6]. It considers the use of an Industrial Internet of Things (IIoT) platform for receiving and storing waste data from the production, to identify abnormalities and/or deviations from pre-defined thresholds. Moreover, scrap is collected in waste containers, whose management becomes important if not efficiently handled during production. Filled-up waste containers may interrupt production until they are either emptied or replaced. Thus, the monitoring and estimation of the fill level become important for preserving a smooth production. This will allow companies to manage their scrap collection process more efficiently, something that can reduce operational costs and environmental impact. This process can be supported by the use of sensors and computer vision methods [7]. The decreasing costs of the Internet of Things (IoT) are trending nowadays, and it can be applied in a wide range of fields. Waste management using IoT has a very good prospect and is required for reducing the growing pollution in the environment [8]. In [9], sonar sensors have been used to monitor wastebins in smart cities to provide measurements from 2 cm to 400 cm, with a 3 mm accuracy. In [10], they used two ultrasonic sensors inside waste bins for collection and monitoring of the level of waste inside the bin. In [11], they deployed infrared and sonar-based sensors for metal waste monitoring in a manufacturing site. Machine and computer vision has gathered intensive academic attention and the use of this technology has spread rapidly, because of its advantages. Machine vision technology with human-like vision capability has revolutionized the process of automated inspection [12; 13].