Depending on the industry and working environment, there may be different requirements for workers to follow. For example, wearing personal protective equipment (PPE), HORECA uniforms, technical maintenance, hospital or laboratory clothes, emergency kit equipment or vehicle equipment.
However, it is hard to keep track of every worker and oversee whether they follow the rules. In order to avoid unfortunate consequences and penalties, digitally transform and automate the way you monitor what is happening within the company.
To help employers gain a clear understanding of the internal processes, CVisionLab’s solution utilized computer vision algorithms.
The method is based on deep neural network capabilities for image pattern recognition. This allowed us to spot workers violating health and industrial safety regulations. However, the scope of application extends to many other areas.
To help employers gain a clear understanding of the internal processes, CVisionLab’s solution utilized computer vision algorithms.
The method is based on deep neural network capabilities for image pattern recognition. This allowed us to spot workers violating health and industrial safety regulations. However, the scope of application extends to many other areas.
84%
The majority of workers who suffered injuries, such as concussions, skull fractures, and scalp wounds, did not wear head protection.
60%
A significant portion of eye injuries was associated with not wearing eye protective equipment.
A combination of regular hazard awareness and timely detection of noncompliance can have a great positive impact. Also, it helps companies and workers avoid significant industry penalties. For instance, there are on-thespot fines issued for work health and safety offenses, up to $7,000 for each violation.
For example, it can either be one of the following conditions or a combination of them:
In our case, a set of PPE consisted of: a hard hat, protective glasses, a reflective vest.
To achieve efficient feature extraction and selection, we compiled a dataset spanning many different object categories. Overall, the number reached about 85,000 images. The images were partially collected from the web. The other part consisted of annotated datasets relying on the collaborative effort of our specialists.
The dataset contained a wide variety of environments and multiple images of different instances of the same class. Alternating shooting conditions and a variety of digital cameras assisted the process further.
99.6%
A major concern we needed to address, for PPE detection, was extreme head positioning. As a person started turning their head sideways, the system stopped recognizing the face within the frame. By using facial detection, we eliminated the problem and achieved 99.6% accuracy.
The results were additionally processed by enforcing temporal consistency. Thus, we minimized instances of misidentified objects caused by unwanted circumstances. For example, the lens could be blocked for a short period of time.
The performance rate can be regarded as fairly high – up to 20 FPS when using a Full HD camera and a middle-tier graphics card, like GeForce GTX 1050 Ti Mobile. Such cards are widely available in averagely priced laptops. Also, this solution provides simultaneous processing of up to 6 people with no effect on performance. If the number exceeds six it can result in a drop in the framerate.
Accuracy | Recall | Precision | ||
---|---|---|---|---|
MobileNet + softmax, hat, Adam, lr=0.01, cat_crossen, crop by yolo with HD | 0.984 | 0.983 | 0.997 | 0.991 |
MobileNet + softmax, glass, Adam, lr=0.01, cat_crossen, crop by yolo with HD | 0.985 | 0.973 | 0.982 | 0.981 |
MobileNet + softmax, vest, Adam, lr=0.01, cat_crosse, crop by yolo with HD | 0.983 | 0.975 | 0.989 | 0.989 |
Accuracy – is the ratio of number of correct predictions to the total number of input samples.
Precision – is the fraction of relevant instances among the retrieved instances.
Recall – is the fraction of the total amount of relevant instances that were actually retrieved.
F-score – is query classification performance.
Our detection method has been utilized for an industrial facility with the aim to ensure continuous monitoring. As evidenced in practice, noncompliance with PPE rules can take many forms. For instance, the system detected a number of workers holding hard hats in their hands. This further proved the necessity to analyze the object placement, rather than its pure visibility.
Once the system identifies non-compliance, it can be programmed to perform different actions: from alerting the Security Service to automatically shutting down the operations.
Control over safety violations used to be limited by the human factor. As much as employers would benefit from assigning a safety officer to every worker, this is not feasible. But when you utilize an automated system, with more than 99% accuracy, you receive the benefits of effective and impartial service.
With recent developments in computer vision, you can implement this system into all kinds of production premises. It does not require costly facilities, such as a separate data center. Lastly, there is no need for an IT department to maintain the system since it is fairly simple to manage.
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