How can Blockchain Improve Computer Vision Systems?
Blockchain technology is a decentralized ledger that records transactions. It is also a distributed database that can be used to store data in a secure and verifiable way. This allows the users to view and edit the data, but not make changes without the consensus of the network. Blockchain technology can be used for many different purposes, including tracking goods through supply chains, managing land registries, and voting systems.
Blockchain can improve computer vision systems in a number of ways. It can be used to store and share data, provide an audit trail for the system, and help with verification of the data.
A blockchain can be used for a wide range of applications, including computer vision systems.
The way a blockchain works is by generating blocks of data, each containing information about the previous block. This means that an image can be turned into a block of data and then stored in the blockchain. The result is a tamper-proof record of what has happened to an image over time, which can then be used for computer vision systems.
Real-World Use of Computer Vision and Blockchain for Supply Chain Monitoring
A) In the Perishable Food Industry
More and more perishable food businesses are adding antimicrobial coatings to the packaging of their products to inhibit bacteria. With the very short shelf life of these products, it’s essential they’re safe and don’t go bad before they can be sold. Many food industry supply chain tracing systems suffer from low scalability, accuracy and readability. This can be overcome with the help of a Blockchain Machine Learning-Based Food Traceability System (BMLFTS). This nifty little tool tackles a lot of issues regarding your warehouse transactions and food. The adaptive DTMS (data tracking management system) helps solve problems such as the food wastage, shipping times, inspections and other things. This system is backed by machine learning algorithms which estimate when a perishable food product will expire. Factors like temperature, oxygen and humidity are monitored to ensure there are no risks of spoilage. You can trust that this information is secure, stored in a database and accessed by professional business that want to make sure the shelf life of their products is extended. Machine learning has a fuzzy logic, as well as real-time data analysis and predictive analysis to monitor your supply chain. Waiting for data from a spreadsheet is so 2016. The purpose of all these tools being used together is for the durability of supply chains; such as to make sure there are no missing values, irrelevant information, and any tampering of products.
B) In the Textile Industry
Businesses involved in textile manufacturing can use computer vision to ensure the quality of their products from the first step through to completion so that when the product is finished, it’s of high-quality with no errors. Computer vision tools and image processing software are used for quality inspection. These tools include smart cameras which closely monitor material quality and provide feedback to procurement managers about it. Image processing software is also used for inspecting the quality of materials in fast-moving textile assembly lines Machine vision is a technique widely used in the manufacturing of textiles. It scans the textile material to look for defects. Computer algorithms in this technique run several checks on each pixel to make sure they meet necessary standards, thereby detecting any structural defects there might be.