In the Era of Monitoring Only Hard Hats, REAL Risks Grew in the Gaps

At construction and manufacturing sites, not wearing a hard hat has been considered a representative safety issue. However, real risks are hidden in a much wider area — gloves, boots, safety glasses, gas masks, air-line masks, etc., all vary depending on the type of work. Added to this, managing environmental safety equipment like tripods and barriers makes the complexity of the field beyond imagination.

Past AI only judged 'the presence or absence of a hard hat'. But EVA is different. EVA learns all the 'contexts' of the field and is evolving beyond single object detection into comprehensive safety judgment by scenario. This case shows how EVA is actually making that change.

Background: "Monitoring That Only Looked for Hard Hats Was Different from Field Reality"

A safety manager's day always starts with the same concern. "Who is working without gloves today?", "Is that person entering without a gas mask over there?" Construction and manufacturing sites change environmental conditions every moment. In the morning, a welder might work without safety glasses; in the afternoon, a chemical handling worker without a mask and protective clothing; in the evening, a high-altitude worker without a safety belt.

Existing CCTV+AI systems were designed centered on 'not wearing a hard hat'. Other PPE could not be judged properly, and ultimately had to be checked by human eyes. For safety managers who had to check all PPE individually across dozens of screens, this was a chronic difficulty.

Above all, required PPE varied by work type. Welders need safety glasses, chemical workers need masks, and high-altitude workers need safety belts. Since a single AI could not distinguish all these situations, the field always had to accept the limit of "AI that only catches hard hats."

Then came the cry from the field — "AI that only looks for hard hats cannot protect the site."

The customer needed a solution that went beyond single detection. AI that recognizes required PPE by work type and is capable of practical safety judgment. EVA was highlighted as the answer.

The Clue to the Solution: EVA's Core, 'Prompt-based Scenario Definition' (Why EVA?)

The core of EVA is 'prompt-based scenario definition'. For example, if you define PPE combinations by work scenario as a prompt, such as "Welder = Safety glasses required" or "Chemical handling = Mask required," EVA immediately detects missing PPE according to the situation.

If existing AI stayed at a 2D judgment of 'hard hat on/off', EVA even understands context, like "Welder but no safety glasses → Danger." This structural difference was the key factor in deciding the PoC.

The Process of Problem Solving: "EVA Began to Understand the Field and Judge on Its Own"

In the first stage of PoC, the customer set up PPE scenarios so that EVA could understand the field.

Step 1: Scenario Prompt Definition

• Scenario 1: Welding work → Safety glasses + gloves required
• Scenario 2: Chemical handling → Mask + gloves + protective clothing required
• Scenario 3: High-altitude work → Hard hat + safety belt required

Step 2: Real-time Detection

EVA sequentially judges worker's work type → required PPE → actual wearing status from CCTV video. If it's a "Welder but no safety glasses," it immediately sends a notification.

Now safety managers don't need to check all screens directly. Because EVA automatically notifies "who, in what work, what PPE is missing." The complex human-centered verification structure was converted to EVA's scenario-based automatic judgment structure.

The Possibility Proved by PoC: "PPE Detection Evolving with Prompts and Feedback"

During the PoC, EVA showed continuous accuracy improvement based on actual field feedback. Customers personally experienced the process of EVA evolving into an 'AI that understands the field' beyond simple detection.

• Stress Reduction: "It takes care of not only hard hats but also air-line masks and safety glasses. It catches situations where someone works without a gas mask, so I feel relieved."
• Work Efficiency: In the past, all screens had to be checked individually, but after the introduction of EVA, you can focus only on notification zones.
• Accuracy Improvement: If an incorrect detection occurs, and the manager provides "worn" / "not worn" feedback, EVA immediately reflects that image to increase accuracy from the next detection.

The biggest expectation is this. "A perfect PPE detection system can be built with a combination of prompt optimization and feedback."

Through PoC, EVA is evolving from judging a single piece of PPE to recognizing all PPE, and further to comprehensive judgment by situation. Safety management is now moving from the era of 'post-response' to the era of 'proactive prevention'.

Do You Have This Concern at Your Site Too?

"AI that only looks for hard hats is not enough. I want to manage all PPE and safety equipment in the field at once."

EVA remembers all different PPE for each worker and situation. It understands the context of the field through scenario prompts and performs safety judgments appropriate for the actual work environment.

Build your site's own PPE system through EVA PoC now. Check for yourself how much safety management work becomes lighter.