Calibrated on the 117-frame TC260-producer cohort (vendor_cohort_harvest +
vendor_mark_calibrate, both committed here): per-mark 2-rung ladder
(0.78, 1.27) for the two measured size modes, fitted locate box (the mark
sits ~0.025 of the short side off the edge; doubao's box clipped the first
glyph), measured template aspect 0.26, gate 0.45 (clean p99 0.301).
Strict-only (the sub-gate band is non-Qwen banners), no rival margin
(0 cross-fires on 400 doubao / 298 jimeng / 286 clean frames).
83/83 real marks detector-clean after cv2 fill.
TextMarkConfig gains a per-mark ladder field; the shipped 3-rung default
is unchanged for every other mark.
The faint-mask fallback added for the tophat front-end thresholded the
max-normalized uint8 response at 0.5 -- which selects every non-zero pixel,
not "half the peak" as its comment claimed -- and filled ~120% of the corner
box on textured frames. Measured on 14 real faint-path frames (cv2 fill,
detector re-run after): the detector's own best-match box fills a 58.7%-median
corner box vs 120.9% for the threshold, both 100% detector-clean. Detection and
the mask now read one method, _tophat_best, whose score gates detection and
whose argmax box bounds the fill, so the two cannot drift by construction --
which is how the mismatch arose. The 0.5 constant is deleted.
Parity could not catch this (a mask that fills everything is trivially
detector-clean) and the regression test could not either: its flat fixture
gives every threshold the same box, so mutating the constant to 99.0 stayed
green. The fixture now carries texture and asserts the mask area is bounded,
not merely non-empty; it reproduces the corpus number (127% pre-fix).
Also lands the Tier B2 verification harnesses that found and bounded this:
- detector_response.py: response curves (detected AND maskable per cell); found
the size response is a comb, contrast is near-irrelevant, no unmaskable cells.
- ladder_headroom.py: measured that a denser scale ladder recovers 7.6% of
misses for a 2.52%->3.05% false-fire rise, and the one landscape rung that
helps is a geometry shift that helps and hurts equally (1.7:1) -- do not add.
- cjk_tail_probe.py: a generic shared-tail (AI生成) template does not separate
uncovered vendors from clean corners (0.407 vs clean p99 0.298).
Records the visible-parity re-run confirming the earlier front-end fix (doubao
91.8% -> 99.3%), and dedups the thrice-written stamp forward model into one
fill_quality.composite.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Victor Kuznetsov