A quality-labelled dataset helps in creating a path where you can build a model which provides better output results and get rid of Gargabe In Garbage Out cases. Accurate data labelling ensures better quality assurance within machine learning algorithms, allowing the model to train and yield the expected output.
Whether you train a model with the data having YOLO format or COCO format or even the classic PASCAL VOC XML format. We provide all types of export formats to help you train any kind of model with ease.
Your ML model largely depends on the dataset that you are training on and training on less accurate dataset results in poor outcomes in the real world. Whereas accurately labelled data can help you achieve not only good results but also stands out in real-world scenarios as well.
Companies necessitate continuously upgrade their existing infrastructure and methods/rules to be more efficient and provide safety for employees. Working in an industrial atmosphere has always been dangerous/critical, and factory managers attempt to find the best solutions to reduce fatalities. The manufacturing or construction industries are the beating heart of any strong economy. To maintain that heart beating, organizations need to manage their workers safely. Safe workers are more comfortable, healthier, and more productive.
These enterprises need workers to engage in high-risk actions such as soldering, welding, metal cutting, raw material assembling, and heavy lifting and rigging. Furthermore, magnetic fields, compressed gases, and harmful radiation can negatively affect a worker’s health. Workplace hazards lead to approximately 150 deaths per day in the U.S. Naturally, these industries’ processes involve many hazards.
We assume that recent progress in AI (or Deep Learning and other related technologies) will help stimulate this trend towards automation more engagingly. This is because AI attaches one very significant component that the companies/firms have been avoiding or missing until today — “The strength of machines to understand and recognize.” that special model is workers’ safety thing detection is to identify the workers in the image/video stream wearing industrial safety things such as goggles, helmets, etc., or not. If workers wear safe things, then determine wearing properly or not with the help of AI techniques like computer vision and deep learning algorithms.
We collect several images of people who are not wearing masks, wearing masks, and not properly wearing a mask. Then we extract images of Industrial workers for detecting safety things such as goggles, PPE, helmets, Etc. We extract all these images by extracting them from different sources, such as social media platforms and machines (cameras) of different resolutions. Then our annotation teams annotated the extracted images with correct label kinds such as masks, no mask, and not wearing them correctly, goggles, PPE kit, helmets, etc.
Frontline professionals in the oil and gas sector have been recipients of many lethal chemical hazards for a long time. While drillers and chain hands come under the banner of security guidelines systems and are responsible for emergency wearables, tool hands and other contractors also need to be lawfully protected from workplace accidents. So heads of the oil&gas industries, such as supervisors, decided to integrate PPE kit detection systems into their existing CCTV cameras. Then those CCTV cameras becoming as a supervisor, those will monitor every employee and warn when an employee misses or neglect their PPE kit.
PPE aims to mitigate workers’ exposure to risk once the engineering and administrative control measures are not possible or enough to reduce the risk level. There is no denying that infrastructure projects pose a great deal of danger. PPE is equipment that protects workers against risks at work. It has several types as per its role, including hearing safety, eye protection, respiratory safety, shielding clothing for protection of the skin, helmets, and rescue belts. The most common accident-related to a construction site with 29 deaths occurring rate is falling from a high place due to negligence and improper PPE implementation.
So construction industries decided to hire a separate person to oversee whether every worker in construction is wearing a PPE kit properly or not. But that is not that easy, like no one can’t observe every minute and every worker on the construction site. So after appointing a special person also accidents rates are not decreased. So then, construction industries decide to automate this process by using any advanced technology. So industry owers are trying to adopt AI-based PPE kit detection models into their CCTV cameras. Those systems will monitor workers each and every minute and send warn/alert messages to the worker and respective supervisors if any person is found without a PPE tool kit thing.
The scope of operations in the manufacturing area needs no introduction; the business is one of the most significant business areas requiring the heavy deployment of personal protective gear. Given that the manufacturing division involves several main and auxiliary rules for welding and metal fabrication, workers are often presented to an environment where they need to switch from hard hats to face shields and welding helmets to protection goggles as per the work profile.
Safety and security concerns in the workplace are major concerns that employers are always looking to address. Therefore, robust workplace safety practices and procedures are a crucial element of manufacturing procedures. To limit the risk of accidents and reduce the chances of injuries, PPE detection plays an important role in the workplace. This Al-enable PPE kit detection embedded system helps to detect whether workers are wearing their safety vests and helmets properly or not. For example, suppose workers are not wearing their respective PPE kits while they are working. In that case, an AI-based PPE kit detection system will alert the worker by sending messages and also send a report to the supervisor with worker details.