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Machine vision and edge AI notes — optics, lighting, algorithms, edge inference

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  • Optics
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  • About
  • Contact
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Categories

  • Edge AI (12)
  • Lighting Design (9)
  • Optics (22)
  • Vision Algorithm (23)

Recent posts

  • AOI Only Says “Defective” — How Vision Language Models Fill the Gap in Judgment Rationale 2026-09-17
  • There Are Defects That Vanish in Visible Light — Inspection Design Beyond the 1 μm Barrier in SWIR Imaging 2026-09-16
  • Catching 20 μm Sink Marks in High-Gloss Injection-Molded Parts with Phase Measuring Deflectometry (PMD) — The Last Gap Polarization Fusion Fills 2026-09-15
  • Where the μm Step Disappears: Measuring Both-Side Thickness Simultaneously with a Two-Channel Chromatic Confocal Controller 2026-09-10
  • Camera Interface Bandwidth Determines Resolution — From GigE Vision to CoaXPress 2.0 2026-09-09
Vision Algorithm

Shape Defects 2D Can’t Catch: When 3D+AI Inspection Is the Answer

SICK's Nova combines deep learning with 3D height-map data to catch shape defects 2D inspection misses, with on-device retraining from a handful of samples.

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2026-08-18
Edge AI

What Changes in Vision Inspection When Logistics Automation Moves Into the Front End

As front-end semiconductor logistics automation expands, vision-based position checkpoints at transport handoffs become the standard defense against alignment drift.

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2026-08-18
Optics, Vision Algorithm

What 2D Contrast Can’t See: How 3D Structured-Light Inspection Measures Solder Joint Height

Two connector leads that look nearly identical in a 2D image can differ entirely in height. 3D structured-light inspection reconstructs actual surface shape instead of relying on contrast, but specular…

read more
2026-09-05
  • 비전 언어 모델이 추가 뷰·확대·정상 참조 중 어떤 증거를 더 볼지 선택하고 예산 게이지가 소진되는 구조를 나타낸 개념도
    Edge AI,  Vision Algorithm

    AOI Only Says “Defective” — How Vision Language Models Fill the Gap in Judgment Rationale

    Noctvision / 2026-09-17 / 0 Comments

    AOI systems report only pass or fail, never why. This article examines how vision-language models can supply the missing judgment rationale, turning an inspector's tacit expertise into structured, auditable evidence.

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  • SWIR 투과 조명 검사 셋업 — SWIR LED 백라이트·디퓨저·다층 필름·SWIR 렌즈·InGaAs 카메라의 광경로 도식
    Lighting Design,  Optics

    There Are Defects That Vanish in Visible Light — Inspection Design Beyond the 1 μm Barrier in SWIR Imaging

    Noctvision / 2026-09-16 / 0 Comments

    A defect with no contrast in visible light must be seen by changing the wavelength. This article maps how SWIR imaging works, how Cu-Cu bonded sensors opened up pixel pitch, and how lighting…

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  • 위상측정 편향법 셋업에서 스크린의 정현파 줄무늬가 고광택 사출품 표면에 반사되며 싱크마크 함몰부에서 일그러지는 광경로와 편광 카메라 구성을 보여주는 백색 배경 개념도
    Optics,  Vision Algorithm

    Catching 20 μm Sink Marks in High-Gloss Injection-Molded Parts with Phase Measuring Deflectometry (PMD) — The Last Gap Polarization Fusion Fills

    Noctvision / 2026-09-15 / 0 Comments

    A shallow depression on a specular surface creates almost no contrast under diffuse illumination. This article maps how PMD turns slope into the measurand, and how polarization fusion resolves the ambiguity a single…

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  • 색채 공초점 센서의 축상 색수차 원리를 보여주는 개념도, 백색광이 렌즈를 통과하며 파장별로 다른 거리에 초점을 맺고 대상 표면에 초점이 맞은 파장만 핀홀을 통해 분광기로 돌아가는 백색 배경 광로도
    Optics,  Vision Algorithm

    Where the μm Step Disappears: Measuring Both-Side Thickness Simultaneously with a Two-Channel Chromatic Confocal Controller

    Noctvision / 2026-09-10 / 0 Comments

    A 2 μm coating deviation is invisible to a 2D vision camera because neither color nor shape changes. This article covers how chromatic confocal converts axial displacement into a spectral signal, and how…

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  • GigE Vision 1링크·5링크와 CoaXPress 2.0의 대역폭 차이에 따라 검출 가능한 최소 결함 크기가 달라지는 것을 비교한 개념도
    Edge AI,  Vision Algorithm

    Camera Interface Bandwidth Determines Resolution — From GigE Vision to CoaXPress 2.0

    Noctvision / 2026-09-09 / 0 Comments

    Six 12MP cameras on a PCB inspection line hit a cable bottleneck before lens or lighting even matter. This article maps how GigE Vision versus CoaXPress 2.0, and edge AI on-device inference, trade…

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  • few-shot 이상탐지의 프로토타입이 반복될수록 정상 경계가 좁아지는 과정을 보여주는 3단계 개념도
    Edge AI,  Vision Algorithm

    Few-Shot Anomaly Detection: Can 5 Defect Samples Stand Up an Inspection Line

    Noctvision / 2026-09-08 / 0 Comments

    A question repeats every time a new line is set up: how many defect samples before an inspection model can run? Few-shot anomaly detection uses mostly normal data plus 5-20 defect samples to…

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  • 전기습윤 리퀴드 렌즈 내부의 절연 오일과 전도성 수용액 경계면이 전압에 따라 곡률을 바꾸는 단면 개념도
    Optics,  Vision Algorithm

    Liquid Lens Autofocus: Solving the Focus Problem on High-Mix, Low-Volume Production Lines

    Noctvision / 2026-09-07 / 0 Comments

    Focus subtly drifting every time a lot changes is a familiar problem on PCB and injection-molded part lines. Electrowetting liquid lenses cut refocus time to within tens of ms, but they are not…

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  • 이벤트 스트림에서 결함과 연관된 이벤트 버스트를 시간창(Δt=1~5ms)으로 구분해내는 과정을 보여주는 개념도
    Optics,  Vision Algorithm

    Ultra-High-Speed Micro-Defects Caught by Event Cameras (Neuromorphic Vision) — Seeing What Frames Miss

    Noctvision / 2026-09-06 / 0 Comments

    A wobbling connector pin, a flicking solder ball, a vibration-shifted alignment — these moments pass between frames on an ordinary camera. Event cameras (DVS) capture only brightness changes, at microsecond resolution.

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  • 2D 콘트라스트 검사에서는 구분되지 않는 리드 들뜸이 3D 구조광 높이 맵에서는 색상으로 뚜렷이 구분되는 것을 비교한 개념도
    Optics,  Vision Algorithm

    What 2D Contrast Can’t See: How 3D Structured-Light Inspection Measures Solder Joint Height

    Noctvision / 2026-09-05 / 0 Comments

    Two connector leads that look nearly identical in a 2D image can differ entirely in height. 3D structured-light inspection reconstructs actual surface shape instead of relying on contrast, but specular materials like solder…

    read more
  • FP32 32비트 특징맵과 INT8 8비트 양자화 특징맵을 나란히 비교해 저콘트라스트 결함의 미세 신호가 양자화 계단에 묻히는 과정을 보여주는 개념도
    Edge AI,  Vision Algorithm

    What You Must Check Before Applying INT8 Quantization to Edge AI Inference: Why Micro-Defects Quietly Disappear

    Noctvision / 2026-09-04 / 0 Comments

    A micro-scratch detection model quantized for edge inference looks fine on overall mAP, but a specific low-contrast defect class can quietly lose accuracy. Layer-wise quantization, edge hardware, and camera interface all interlock in…

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Categories

  • Edge AI (12)
  • Lighting Design (9)
  • Optics (22)
  • Vision Algorithm (23)

Recent posts

  • AOI Only Says “Defective” — How Vision Language Models Fill the Gap in Judgment Rationale 2026-09-17
  • There Are Defects That Vanish in Visible Light — Inspection Design Beyond the 1 μm Barrier in SWIR Imaging 2026-09-16
  • Catching 20 μm Sink Marks in High-Gloss Injection-Molded Parts with Phase Measuring Deflectometry (PMD) — The Last Gap Polarization Fusion Fills 2026-09-15
  • Where the μm Step Disappears: Measuring Both-Side Thickness Simultaneously with a Two-Channel Chromatic Confocal Controller 2026-09-10
  • Camera Interface Bandwidth Determines Resolution — From GigE Vision to CoaXPress 2.0 2026-09-09
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