Classification of unlabeled battery on X-ray images using machine learning methods

The problem of identifying and classifying hazardous and valuable species of municipal solid waste (MSW), especially unlabeled cell batteries, has become increasingly important in the light of current global environmental policies, which emphasize the need for increased recycling and utilization of waste. With the introduction of a variety of environmental initiatives, it is essential to ensure that proper identification and classification of MSW is carried out in order to reduce the environmental impact of MSW. This includes identifying and classifying hazardous and valuable materials, such as cell batteries, to ensure they are reused and recycled rather than disposed of in landfills. Furthermore, the development of effective strategies for the detection and classification of MSW is essential in order to maximize the economic and environmental benefits associated with the recycling and utilization of waste. This article describes an approach to the standard-size, unlabeled cylindrical cell batteries identification, powered by computer vision. To achieve this goal, a video camera and an X-ray machine are used to analyze and process images. The images captured by the video camera are first processed by a series of steps involving data preprocessing, feature extraction and model training. All the extracted features are then combined to form a model which can be used to accurately detect and recognize the cell batteries in the MSW stream on the conveyor belt. The developed procedures ensure a sufficiently high-quality classification of intact label batteries, and thus can be used to effectively identify the batteries in multiple scenarios. An extra step of digital radiography image processing is proposed, which allows for recognition even when the marking is significantly damaged. This novel approach to image processing offers a dependable and accurate method for the classification of batteries, even when their markings are no longer clearly visible or are completely obscured. This is a great benefit, as previous techniques relied on the clarity of the markings, which created difficulties when those markings were faint or absent. The core of the batteries identification system is a neural network, trained on a data set containing X-ray images of various types of batteries along with the associated classes. This neural network MobilcNctV2 is used to extract features from the images, allowing it to correctly classify the batteries for further sorting.