Trackware developed an innovative AI-based vision system for inspecting crates used in the food industry. Designed to automate the tedious task of manual crate inspection, this system integrates seamlessly with Elpress B.V.’s industrial washing machines. Using machine learning and advanced image recognition, the system identifies crates as clean, dirty, or damaged, improving the efficiency and accuracy of the cleaning process.
The manual inspection of crates after industrial cleaning is a labor-intensive, monotonous task that takes place in difficult working conditions. Elpress B.V., a leading manufacturer of industrial hygiene equipment, needed an automated solution to ensure food industry crates were thoroughly clean and free of contamination, while also reducing the strain on human workers.
Trackware implemented a machine learning-based vision system to automatically inspect crates. Utilizing TensorFlow and custom lighting solutions, the system captures high-quality images of crates and applies a neural network model trained on thousands of images to accurately classify crates as clean, dirty, or damaged. The process involved overcoming challenges like crate misalignment and the variety of crate designs by developing a specialized edge detection algorithm.
The system now automates the inspection process, accurately identifying dirty or broken crates and signaling the Elpress washing machines to eject them for further cleaning or repairs. This innovation has significantly reduced the need for manual inspection, increasing productivity and ensuring crates meet the high cleanliness standards required in the food industry.
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