PCB Defect Inspection Station (YOLO + PyQt6)

A desktop inspection station for printed circuit boards. It runs a YOLO detector over a board image, matches what it found against a reference profile of expected components, and returns a PASS / FAIL verdict with the missing, wrong and extra parts listed.

Companion desktop app to PCB_DETECT_RMUTT. Undergraduate project at RMUTT (Rajamangala University of Technology Thanyaburi).

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What it does

Detection alone doesn't tell you whether a board is good. This app adds the comparison step:

1. Detect — YOLO inference over the loaded image → raw detections (x, y, label, conf, box)
2. Match — each reference point is greedily matched to its nearest unclaimed detection. If that detection is within match_dist pixels and carries the right label, it's OK. Right place but wrong class is reported as WRONG, not MISSING — a distinction that matters when a board is populated but populated incorrectly.
3. Verdict — PASS / FAIL plus ok / missing / wrong / extra lists
4. Log — appended to inspection_log.csv (time, station, operator, verdict, counts)

The reference profile lives in Refs.json as {"x", "y", "label"} points and is edited in the app by clicking on the board in edit mode.

Running

pip install -r requirements.txt
python3 main_program/gui_test.py

gui_test.py is the application entry point despite the name — there is no automated test suite in this repo; verification is manual against test/pass.jpg and test/fail.png.

Other scripts:

python3 prepare.py    # split main_label/ into train/val (80/20, moves files in place)
python3 train.py      # train (edit weights + hyperparams inside the script)
python3 test.py       # single-image inference
python3 camera.py     # standalone webcam detection

Requirements

Pinned to the tested Python 3.14 venv:

ultralytics==8.4.92 · opencv-python==5.0.0.93 · PyQt6==6.11.0 · torch==2.13.0 · torchvision==0.28.0 · numpy==2.5.1 · pillow==12.3.0

The YOLO backend is imported lazily — if ultralytics is missing the UI still launches in a degraded view-only state instead of crashing.

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The GUI

A "Clean Industrial Dashboard" layout: a top bar carrying branding and a global TOTAL / PASS / FAIL / YIELD KPI strip, above a three-column body —

• left, 280px — collapsible accordion: Inspection, Camera, Display, Reference Profile, Station & Model
• center — the image viewport, dominant, with a browse bar above it (Open Folder · folder name · ◀ n / total ▶ stepper), a floating PASS/FAIL verdict badge, and an on-demand history panel below
• right, 320px — contextual detection panel, hidden until you click a box

Images arrive by file picker, folder picker, drag & drop (file or folder), or a webcam capture — every route funnels through open_image_path(), which indexes the containing folder so the stepper can walk the rest of it, each step a full inspection.

Overlays are recomposited on every redraw without re-running inference: thin, semi-transparent class-colored detection boxes (IC/connector blue, capacitor amber, resistor emerald, everything else slate) plus small OK / WRONG / MISSING markers at each reference point.

Tunable in the GUI: confidence 1–100% (default 25%), match distance 5–250 px (default 50 px), fail-on-extra toggle, zoom 20–800%.

Architecture

The main window is composed from mixins rather than one monolithic class:

DefectDetectionGUI              main_program/app/window.py
├── InteractionMixin            shortcuts, drag & drop, Ctrl+scroll zoom, fades
├── UIMixin                     builds top bar, panels, viewport, status bar
├── ModelReferenceMixin         lazy YOLO load, Refs.json, undo/redo, toasts
├── InspectionMixin             debounced inference, overlay compositing, verdict reveal
├── BrowseMixin                 folder indexing, Prev/Next, open_image_path()
├── CameraMixin                 threaded webcam preview, capture-then-inspect
├── HistoryMixin                CSV logging, animated counters, yield
└── SettingsMixin               persist/restore settings.json

Qt stylesheets can't animate, so motion lives in Python: styles.py (token-based light/dark design system) · animations.py (fade_in, pulse_glow, animate_number, animate_bar) · components.py (custom-painted YieldBar, BusyOverlay, CollapsibleCard, ToggleSwitch) · toast.py · splash.py

widgets.py holds ReferenceLabel, a QLabel that maps click coordinates from displayed space back to original-image space across zoom levels — click-to-place reference points in edit mode, click-to-inspect a detection otherwise.

The matching algorithm itself is isolated in main_program/app/inspection_logic.py: evaluate_inspection(), draw_detections_overlay(), draw_reference_overlay(), detection_status_map().

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Dataset — 23 classes

ant  button  capacitor  capacitor_0  chip  connector  connector_0  connector_1
connector_2  diode  hole  inductor  input  led  resistor  resistor_8  sdcard
sot21  sot23  sot31  sot32  usb  xtal

Labelled in Label Studio; main_label/ holds the images and YOLO-format labels, prepare.py produces the 80/20 split.

Training

from ultralytics import YOLO

model = YOLO("yolo26x.pt")
model.train(
    data="data.yaml", epochs=300, imgsz=640, batch=16, device=0,
    optimizer="AdamW", lr0=0.001, lrf=0.01, cos_lr=True, patience=50,
    hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=10, translate=0.1,
    scale=0.5, shear=2.0, flipud=0.5, fliplr=0.5, mosaic=1.0, mixup=0.1,
    cache=True, amp=True, workers=8, plots=True,
)

On Colab, mount Drive, os.chdir into the project, pip install ultralytics, and rewrite data.yaml with an absolute path: before training.

The run committed in trained/ is a smoke test, not the real model — args.yaml shows 10 epochs on device: cpu with yolo26n.pt, and results.csv reports mAP 0.0 throughout. Retrain before trusting any numbers from it. trained/best.pt and trained/last.pt are Git LFS pointers; trained/best.onnx is the real exported graph.

Weights	Size	Speed	Accuracy
yolo26n.pt	Nano	Fastest	Lowest
yolo26s.pt	Small	Fast	Low
yolo26m.pt	Medium	Medium	Medium
yolo26l.pt	Large	Slow	High
yolo26x.pt	XLarge	Slowest	Highest

Key files

File	Role
Refs.json	Reference profile — expected components as {x, y, label}
data.yaml	Dataset config, 23 classes
inspection_log.csv	Generated inspection history
trained/best.onnx	Exported model graph