Abstract
Wooden nail-stitched crates are widely used for fruit transportation. Bad stapled nails are transformed into severe product damage that creates stains on the crate due to its juice. In consequence, the final customer depreciates the product because the quality product is in doubt. Human visual inspection of badly stapled nails is a non-effective solution since constant criteria are difficult to reach for all of crate production. A system for the in-line inspection based on a conveyor belt of badly stapled nails in stitched crates is presented. The developed inspection system is discussed with the definition of the computer vision system used to identify fails and the description of image processing algorithms. The experiments are focused on a comparative analysis of the performance of five state-of-the-art classification algorithms based on a deep neural network and traditional computer vision algorithms, highlighting the trade-off between speed and precision in the detection. An accuracy of over 95% is achieved if the user defines the nail location in the image. The presented work constitutes a benchmark to guide deep-learning computer vision algorithms in realistic applications.
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