Update ethics.md (v1.1.0 on-pixel anonymization)

ethics.md

@@ -19,17 +19,27 @@ intended use.
 
 ## Anonymization
 
-Faces in the released frames are anonymized using MediaPipe's long-range
-face detector (`solutions.face_detection.FaceDetection(model_selection=1,
-min_detection_confidence=0.3)`). Detected face regions are blurred with
-a Gaussian kernel (99×99, σ=30) plus 20% padding around the bounding box.
-
-Detection rate on the released set: 4.81% (518 of 10,760 frames had at
-least one detected face). The low rate is structural: cameras are
-positioned 7–10 m from typical worker positions, so faces typically
-occupy <20 px and fall below the detector's reliable threshold. This
-distance also means faces in the unblurred 95.19% are too small to
-support reliable identification.
+### Version history
+
+- **v1.0** (initial release): face blur on the 10,760 JPG frames only, via MediaPipe BlazeFace. The 50 sample MP4s were **not** anonymized; manifests and annotations contained one SAIL railcar identifier and the work-area name "Go Down South Side".
+- **v1.1** (this release, 2026-05-15): added YuNet face blur on all 50 sample MP4s; added an EasyOCR on-pixel text-blur pass on every JPG and every 5th MP4 frame; scrubbed the SAIL railcar identifier from one annotation; renamed "Go Down South Side" → "Warehouse South Side" in 6 annotations + 224 manifest rows + 1 camera-zones row.
+
+### Methods (v1.1)
+
+**(1) Face anonymization — JPGs (carried over from v1.0).** MediaPipe long-range face detector (`solutions.face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.3)`). Detected face regions are blurred with a Gaussian kernel (99×99, σ=30) plus 20% padding. Detection rate on the released JPG set: 4.81%. The low rate is structural: cameras are 7–10 m from typical worker positions, so faces occupy <20 px and fall below the detector's reliable threshold. This distance also means faces in the unblurred majority are too small to support reliable identification.
+
+**(2) Face anonymization — sample MP4s (new in v1.1).** OpenCV YuNet (`face_detection_yunet_2023mar.onnx`, score threshold 0.5), applied to every frame of every MP4 with the same 99×99 Gaussian + 20% padding. We switched to YuNet (rather than re-using MediaPipe BlazeFace) because the MediaPipe `solutions` API was removed in the Python 3.12 wheel; YuNet ships as a 232 KB ONNX model loadable via OpenCV directly. Audio is stripped during re-encoding. Across all 50 MP4s (13,714 frames), 19,306 face-blur applications were made.
+
+**(3) On-pixel text anonymization (new in v1.1).** EasyOCR (English + Hindi/Devanagari) detects all visible text with a confidence threshold of 0.20. Detected text is blurred with a Gaussian kernel (51×51) plus 8 px padding when it matches any of:
+  - Brand/company identifiers: SAIL, BSL, Steel Authority
+  - Indian location words: Bokaro, Jharkhand, India(n), and any Devanagari (Hindi) script
+  - Other Indian steel plant names: IISCO, Durgapur, Bhilai, Rourkela, Jamshedpur
+  - Area-board signage referring to the source plant's internal zone names
+  - CCTV timestamp / camera-ID overlay patterns
+
+In addition, **all** detected text in the top 80 px and bottom 80 px of every frame is blurred regardless of content (these bands typically carry CCTV camera-ID and timestamp overlays).
+
+Across the released frame set: 12,573 sensitive-pattern regions and 10,229 overlay-band regions were blurred in 10,616 of 10,760 JPGs. In the sample MP4s (OCR run every 5th frame): 3,347 sensitive + 2,363 overlay regions were blurred.
 
 **Anonymization is best-effort, not exhaustive.** Specifically:
 - Small faces (<20 px) may not be detected at all and therefore not