1. Industrial Safety Scenario (新增 Section，可直接插入论文)

Section X+1: Industrial Safety-Aware Perception

Problem Context

In safety-critical industrial environments, perception systems are responsible not only for detection accuracy but also for real-time hazard prevention and risk-aware decision making.

Typical scenarios include:

Key Challenge

Unlike standard detection tasks, industrial safety systems require:

early stopping decisions under uncertainty to prevent catastrophic failure

This introduces a fundamentally different optimization objective:

2. Safety-Aware Optimal Stopping Formulation

We extend the original objective:

Safety-Augmented Reward Function

```latex id=”safety1” R(B_t) = R_{task}(B_t) - \lambda_1 C(t) - \lambda_2 R_{risk}(B_t)


where:

* (R_{task}): detection/recognition reward
* (C(t)): latency cost
* (R_{risk}): safety risk penalty

---

##  Risk Function Definition 

We define:

```latex id="safety2"
R_{risk}(B_t) = P(\text{hazard} \mid B_t) \cdot \Omega

where:

Safety-Constrained Stopping Rule

```latex id=”safety3” \tau^* = \inf \left{ t : g(B_t) - \lambda R_{risk}(B_t) \ge V(B_t) \right}


---

#  3. Industrial Safety Use Cases (可直接加到 Experiment)

##  Scenario A: Human–Robot Collaboration Safety

* Detect human entry into robot workspace
* early stopping triggers emergency stop signal

### Decision logic:

```text id="safeA"
If hazard probability > threshold:
    STOP immediately (hard safety constraint)
Else:
    continue perception

Scenario B: Conveyor Belt Hazard Detection

Hazards:

Optimal stopping meaning:

stop observing when system is confident enough to trigger emergency shutdown

Scenario C: High-Speed Equipment Monitoring

Key requirement:

detection delay < 50 ms

Optimal stopping ensures:

Scenario D: Fire / Smoke Early Warning (Edge AI Safety)

Characteristics:

Stopping behavior:

```text id=”safeD” uncertainty high → continue observing hazard probability rising → early stop + alarm


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#  4. Safety-Aware Experimental Extension

（这一段是 TII 审稿人非常喜欢的）

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##  4.1 Safety Metrics 

We introduce safety-specific metrics:

### (1) Time-to-Detection (TTD)

[
TTD = t_{detection} - t_{event}
]

---

### (2) Hazard Miss Rate

[
P_{miss} = \frac{false\ negative\ hazards}{total\ hazards}
]

---

### (3) Emergency Response Latency

[
L_{emergency}
]

---

### (4) Safety Efficiency Score

```latex id="safe_metric"
S = \frac{Accuracy}{Latency \cdot Risk}

4.2 Expected Result Pattern

Method Miss Rate ↓ TTD ↓ Latency ↓
YOLOv8 High Medium High
Temporal CNN Medium Medium High
Ours (OSDIP-Safety) Lowest Best Low

5. Key Contribution

Safety Extension Contribution

We further extend the proposed OSDIP framework to safety-critical industrial environments, introducing a risk-aware optimal stopping formulation that explicitly incorporates hazard probability into the decision boundary.

This enables:

6. Discussion（审稿人最关心的升级点）

✔ Why safety extension matters

传统工业视觉：

detect → output

你的系统：

perceive → estimate risk → decide when to stop → act

✔ Core Insight

In safety-critical systems, optimal stopping is equivalent to optimal intervention timing.

✔ Industrial Impact