Barcodes have played a pivotal role in revolutionizing supply chains since the 1970s. Traditional LED and laser scanners have been the go-to solution for reading barcodes, but with advancements in technology, new possibilities have emerged.
Here, I explore the limitations of traditional scanners and the rise of camera-based barcode scanners empowered by image analysis systems. I will delve into the intricate operations performed by these scanners and their superior efficiency in barcode location and decoding. Additionally, I will discuss the ongoing research in computer vision-based barcode reading techniques and the broader impact of machine vision in logistics beyond barcode scanning.
The limitations of traditional scanners
Traditional barcode readers operate by shining LED or laser light across a barcode, with the reflected beam detected by a photoelectric cell. While simple and effective in their time, these scanners have certain limitations that hinder their performance and restrict their application range. They require prior knowledge of barcode location, struggle with complex scenes, and are unable to read multiple barcodes simultaneously. Moreover, low-quality barcodes pose challenges, potentially leading to losses in time, money, and reputation.
The rise of camera-based barcode scanners
Camera-based barcode scanners, empowered by image analysis systems, have emerged as a game-changer in logistics automation. These scanners perform intricate operations, starting with image acquisition and preprocessing. Images are converted to grayscale, noise is reduced, and barcode edges are enhanced using various filters. Binarization is then applied, isolating black and white pixels for decoding. Unlike traditional scanners, image-based barcode scanners excel in barcode location and decoding. They eliminate the need for prior knowledge of barcode position and can locate and extract multiple barcodes in a single image.
The advantages of optical barcode scanners
As technology progresses, optical barcode scanners are gradually replacing LED and laser-based solutions, offering superior efficiency and performance. Computer vision-based barcode reading techniques have sparked extensive research, addressing challenges in both location and decoding steps. Barcode localization, the most intricate part, involves detecting and extracting barcodes accurately despite illumination variations, rotation, perspective distortion, or camera focus issues. Researchers continually refine barcode extraction techniques, using mathematical morphology and additional preprocessing steps for precise recognition.
Beyond barcode scanning: the impact of machine vision in logistics
The impact of machine vision in logistics extends beyond barcode scanning. Robot-operated warehouses, such as those employed by Amazon, rely on 2D barcodes to navigate shelves efficiently. Drones equipped with computer vision capabilities open new possibilities for delivery services, enabling autonomous and accurate package handling.
Machine vision technology is revolutionizing the way logistics operations are conducted, enhancing efficiency, accuracy, and overall customer experience.