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With the advent of 5G technology for the coming years, smartphones will increase their capacity to contribute directly to computer vision applications. The role of mobile devices has never been as important as it is today. Nowadays, with increasingly intense standards of mobile processing power and camera quality associated with a growing dependence on the cloud, smartphones can get the most out of computer vision. This opens up numerous opportunities for companies, such as back-office process optimization, customer engagement, and automated visual inspection in manufacturing, retail, logistics, and other industries. Let's look below at the most powerful applications of computer vision in mobile apps:
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User Authentication
Simple password-based authentication is becoming too complex and less effective, resulting in weak security, poor user experience, and a high cost of ownership. Security technology combined with machine learning, biometrics, and user behavior will improve usability and self-service features. For example, smartphones can capture and learn a user's behavior, such as patterns when they walk, swipe, press the phone, scroll, and type, without the need for passwords or active authentications.
Mobile Audit
Mobile auditing allows photos to be captured by auditors in the field with a camera-equipped smartphone. Previously, these cameras needed to be synchronized with database records. However, today, audio recordings can be transcribed into text automatically. Thus, auditors can skip the tedious task of entering additional information after returning from the field to the office, thereby facilitating work activities. With a mobile audit checklist, auditors enjoy not only the mobility of their auditing software but also other tools that come with the latest handheld devices. In addition to easy access to cloud databases, auditors can also use device cameras to capture photos that will provide additional verification to audit records. An auditing solution using smartphones equipped with GPS devices also helps to capture geospatial coordinates at the field location where the auditor is, thus providing accurate data related to the audit.
Object and Inventory Counting
Smartphones have the potential to combine Computer Vision and Machine Learning in the most appropriate way possible for the corporate needs of each company. It is possible to build automation with programmed algorithms, aiming at the processing and interpretation of images captured on production lines, according to the demand of each industrial sector. Through these applications, it is possible to count all existing items on an industrial conveyor belt operating on a large scale, thus defining an ideal number of units to be selected or packaged, reducing the risk of inaccuracies in the counting process that lead to an excess or shortage of units in a production batch. The current counting system is based on manual estimation, generating a high number of inaccuracies. With the use of Computer Vision for counting objects and stock, accuracy reaches a rate of 99%, thus ensuring complete monitoring of batches, along with statistical and historical reports generated on a regular basis.
LiDAR on iPhones
The first thing to clarify is that LiDAR is not an exclusive Apple technology, nor is it an invention of this big tech company. LiDAR stands for "Light Detection and Ranging". In other words, it is a method that can detect and measure the distance of objects in the environment. The measurement capability of LiDAR is a highly necessary function for scientists and professionals who rely on precise calculations in their daily lives. This facilitates many tricky tasks such as mapping terrain and forests. Its most prominent use associated with the iPhone is correlated with assisting the drivability of autonomous cars, contributing directly to the safety of self-driving.
Enhanced Product Diagnostics
One of the most exciting computer vision features developed recently is mobile maintenance applications that have increased the ability to perform diagnostics on equipment and products. Most equipment in the industrial environment undergoes degradation that can be visually detected, such as discoloration, rust, broken machine parts, etc. The processors shipped with any standard smartphone have now achieved the capability to process advanced image processing algorithms that can detect the deterioration of peripherals just by taking their photos. Mobile phones can also be used to detect equipment specifications by scanning them through the Quick Response (QR) recognition system. Upon scanning, mobile applications integrated with the server can extract the necessary information for the equipment serial number, such as its fault codes, breakdown history, and maintenance record, thus facilitating technicians in performing the necessary diagnostics and operational restorations.
Fabio Caraça
Fábio Caraça is the Chief Growth Officer at Pix Force. He leads Pix Force's transformation into a scalable SaaS operation, combining strategic vision, culture, and high-impact execution.
