## 2 Computer Vision and Deep Learning Method for Industrial Scrap Containers Fill-Level Estimation  
Based on the results of the state-of-the-art analysis, three main options were identified for industrial scrap containers fill-level estimation. Computer vision systems, ultrasonic sensors, and sensors can measure weight. Ultrasonic sensors are commonly used in industrial environments for measuring the fill level of waste bins and other applications (e.g., [10]). However, the installation of an ultrasonic sensor, in some cases such as the case studied in this work, is not efficient in terms of not disturbing the production processes in a factory. The sensor should have been placed within or above the bin container at an inclined angle. Such a placement would have introduced high risk of the sensor being damaged during the bin filling as well as the bin collection processes. This risk would have required for sensor mounting and unmounting procedures during these processes which is not efficient. Weight sensors for relatively large containers may be considered an expensive and intrusive solution to the working environment. These sensors may also have considerable error and provide inaccurate results in estimating the bin fill-level. Different scrap loads may have the same weight but take up different volumes in the container, because of different load patterns, thus corresponding to significantly different bin fill levels. The cost and maintenance of a vision system for industrial waste bin fill level estimation depends on several factors, such as the type of cameras used, the complexity of image processing algorithms, and the number of cameras required for the application. In this work, a low-cost machine vision system was developed as low-cost area scan cameras, using a software triggering mechanism and no frame grabber hardware. These systems can provide accurate results about the fill level and can be less affected by factors such as the fill pattern of the scrap in the bin, and are not intrusive to the working environment. This work presents a novel computer vision system that can automatically recognize the fill level of industrial waste containers with metal scrap. The fill pattern may differ in terms of waste location within a container, geometry and shape of the waste, and relative position compared to the optical sensor (camera). The proposed approach can classify the fill level in different categories by capturing RGB images of the waste containers and applying them as input to a pre-trained CNN model that executes the classification task. The results of the fill-level estimation process are managed by an IIoT platform and visualized in a user-friendly web application. In Figure 1 the flow diagram of the proposed method is presented. Figure 1: Flow-diagram of the proposed method. 1. _Define bin fill levels_: This step decides on the definition of the fill levels of the waste bins. The fill level ranges from 0% (empty bin) to more than 100% (overfilled bin). Moreover, as a special class, the \"no-bin\" class can be included, signifying the absence of the container within the field of view (e.g., the container was previously moved by a forklift). The granularity of the fill-levels from 0% to 100% is defined by taking into account field experts' capability to classify the fill level of a container as well, as the level of accuracy needed by the estimation model for offering waste management services on top of the fill-level assessment. In this work, as explained in the industrial case description, a step of 10% has been adopted so that the levels were (1-10%), (11-20%) etc. These levels are then used for labelling captured images for model training purposes. 2. _Image data acquisition and pre-processing_: Images are collected by optical sensors. Pre-processing of image data takes place to make sure that the image fits the needs of the classification task. In some cases, cameras may serve multiple purposes and are not used solely for fill-level monitoring.