This scientific article presents the implementation of an automated control system for detecting and classifying faults in tuna metal cans using artificial vision. The system utilizes a conveyor belt and a camera for visual recognition triggered by a photoelectric sensor. A robotic arm classifies the metal cans according to their condition. Industry 4.0 integration is achieved through an IoT system using Mosquitto, Node-RED, InfluxDB, and Grafana. The YOLOv5 model is employed to detect faults in the metal can lids and the positioning of the easy-open ring. Training with GPU on Google Colab enables OCR text detection on the labels. The results indicate efficient real-time problem identification, optimization of resources, and delivery of quality products. At the same time, the vision system contributes to autonomy in quality control tasks, freeing operators to perform other functions within the company.

Brian Bussard has 25 tiny chips in his brain. They were installed in February 2022 as part of a study testing a wireless device designed to produce rudimentary vision in blind people. Bussard is the first participant. Bussard, who's 56, lost vision in his left eye at age 17 after his retina detached. The right eye followed in 2016, leaving him completely blind.

Computer vision is also referred to as artificial vision. It is considered to be a scientific discipline and includes diverse techniques to gather, process and evaluate real-world images. Then these are converted into valuable data. This way, useful information is processed in computerized form. When applied correctly it can help improve business manifolds.

In this paper we consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices, and benchmark them for a real problem against their classical counterparts. Specifically, we consider two approaches: a quantum Support Vector Machine (QSVM) on a universal gate-based quantum computer, and QBoost on a quantum annealer. The quantum vision systems are benchmarked for an unbalanced dataset of images where the aim is to detect defects in manufactured car pieces. We see that the quantum algorithms outperform their classical counterparts in several ways, with QBoost allowing for larger problems to be analyzed with present-day quantum annealers. Data preprocessing, including dimensionality reduction and contrast enhancement, is also discussed, as well as hyperparameter tuning in QBoost. To the best of our knowledge, this is the first implementation of quantum computer vision systems for a problem of industrial relevance in a manufacturing production line.

Artificial vision could be closer to reality, after scientists develop a tiny electric eye designed for use by microbots, which could ultimately help blind people too. Georgia State University researchers created the device using a new vertical stacking system, allowing it to be scaled down, and operate at micro-levels. The goal of the team, led by assistant physic professor Sidong Lei, is to create a micro-scale camera that could operate as the eyes of tiny robots, able to access areas humans, and larger scale bots can't reach. In the future, the team say the same technology could be adapted to bring vision to the blind, or improve color perception in the colorblind. The device makes use of synthetic methods to mimic the biochemical processes that allow humans to see, a step towards a micro-scale robot camera.

Artificial Intelligence (AI) uses a form of machine learning is an application engineered to learn like human beings and to simulate their actions. A subgroup of computer science is machine learning, which originally referred to the notion that computer algorithms can automatically learn from and adaptable to changes data without any assistance of humans. The algorithm chooses behaviors from a list of means--in the case of a basic robot that can consist of PICKUP, PUTDOWN, MOVE FORWARD, MOVE BACK, MOVE LEFT, and MOVE RIGHT--until the objective is attained. Three phases of mobile robot advancement for the Mars Rover Research Project: (A) Genghis, (B) Attila, and (C) Pebbles, shown in MIT's development of a mobile robot to recognize the Martian surface. At present, artificial vision is sufficiently advanced to allow optical sensors to recognize individuals, autonomous vehicles to drive at the maximal intensity on the actual road, and robots to travel through buildings collecting empty coke cans.

A retinal implant with more than 10,000 electrodes has been developed which could give blind people a form of vision. The implant connects wirelessly to a computer system which is in the frame of a custom-built pair of glasses that the person also wears. A camera attached to the frame beams signals to the implant via this computer and electrodes light up accordingly. Illuminated electrodes activate the eye's sight cells which sends an image to the brain. Vision comes in the form of black and white dots which, although vastly different to true sight, would allow people to distinguish shapes and, ultimately, objects. The technology is in the process of getting medical approval for humans and as yet has not been trialled in people.

Two monkeys are able to "see" and recognise letter shapes generated by arrays of electrodes implanted in their visual cortex rather than relying on light hitting their retina. It is the highest resolution achieved with implants in the brain, rather than the retina. "That's really good news," says Pieter Roelfsema at the Netherlands Institute for Neuroscience, whose team aims to restore some vision to people who have lost their sight. Many research groups around the world are working on restoring some sight in people who are blind by sending signals from a head-mounted camera to arrays of electrodes that stimulate the appropriate nerve cells. There have been numerous trials in people already, and one 60-electrode device, called the Argus II, was approved for use in the US in 2013.

From left to right, Zongfu Yu, Ang Chen and Efram Khoram developed the concept for a "smart" piece of glass that recognizes images without any external power or circuits. The sophisticated technology that powers face recognition in many modern smartphones someday could receive a high-tech upgrade that sounds -- and looks -- surprisingly low-tech. This window to the future is none other than a piece of glass. University of Wisconsin–Madison engineers have devised a method to create pieces of "smart" glass that can recognize images without requiring any sensors or circuits or power sources. "We're using optics to condense the normal setup of cameras, sensors and deep neural networks into a single piece of thin glass," says UW-Madison electrical and computer engineering professor Zongfu Yu.

For nearly 100 years, we have understood the idea that it might be possible to restore sight to those who have become blind through a device that delivers electrical stimulation to the brain [Mirochnik, Pezaris, 2019]. Visual prostheses, as they are called, form part of a constellation of approaches that seek to deliver input to the brain to replace a lost or missing sense, including cochlear implants for the deaf, and cortical implants for the insensate, such as amputees with robotic arms. The challenges faced by each approach are similar: biological compatibility, long-term functional stability, and interpretability of the evoked sensations. Biological compatibility has thus far been addressed by careful selection of materials and implant techniques, but much remains to be done to create devices that the body will tolerate for decades with a low risk of infection or rejection. The first major challenge is long-term functional stability; ensuring that the effectiveness of the devices do not degrade over time.