To implement deep learning for image-based defect detection in electrical components (e.g., transformers, circuit boards, insulators) using MATLAB

To implement deep learning for image-based defect detection in electrical components (e.g., transformers, circuit boards, insulators) using MATLAB, follow this structured workflow with code examples:

1. Data Preparation

1.1 Collect and Organize Data

Defective vs. Non-Defective Images: Organize images into folders (e.g., defective, non_defective).
Example Dataset: Use images of components with cracks, corrosion, or soldering defects.

% Load image data using an imageDatastore
imds = imageDatastore('dataset', ...
    'IncludeSubfolders', true, ...
    'LabelSource', 'foldernames'); % Labels: 'defective'/'non_defective'

1.2 Preprocess and Augment Data

Resize images and augment to handle limited data:

% Resize images to 224x224 (for compatibility with pretrained networks)
imds.ReadFcn = @(x) imresize(imread(x), [224 224]);

% Augment data (flips, rotations, brightness adjustments)
augmenter = imageDataAugmenter( ...
    'RandXReflection', true, ...
    'RandRotation', [-20 20], ...
    'RandContrast', [0.8 1.2]);
augmentedImds = augmentedImageDatastore([224 224], imds, ...
    'DataAugmentation', augmenter);

2. Model Selection

2.1 Transfer Learning with Pretrained CNNs

Use a pretrained network (e.g., ResNet-50, AlexNet) and fine-tune it.

% Load pretrained ResNet-50
net = resnet50;
lgraph = layerGraph(net);

% Replace final layers for binary classification
newLayers = [
    fullyConnectedLayer(2, 'Name', 'fc', 'WeightLearnRateFactor', 10, ...
                        'BiasLearnRateFactor', 10)
    softmaxLayer('Name', 'softmax')
    classificationLayer('Name', 'classOutput')];
lgraph = replaceLayer(lgraph, 'fc1000', newLayers(1));
lgraph = replaceLayer(lgraph, 'fc1000_softmax', newLayers(2));
lgraph = replaceLayer(lgraph, 'ClassificationLayer_fc1000', newLayers(3));

2.2 Train/Test Split

Split data into 80% training, 20% testing:

[trainImds, testImds] = splitEachLabel(imds, 0.8, 'randomized');

3. Model Training

3.1 Training Options

Configure hyperparameters:

options = trainingOptions('adam', ...
    'MiniBatchSize', 32, ...
    'MaxEpochs', 15, ...
    'InitialLearnRate', 1e-4, ...
    'ValidationData', testImds, ...
    'Plots', 'training-progress', ...
    'ExecutionEnvironment', 'gpu'); % Use GPU for faster training

3.2 Train the Model

defectDetector = trainNetwork(augmentedImds, lgraph, options);

4. Model Evaluation

4.1 Predict and Analyze

% Predict on test data
predLabels = classify(defectDetector, testImds);
trueLabels = testImds.Labels;

% Confusion matrix
confMat = confusionmat(trueLabels, predLabels);
confusionchart(confMat, {'Defective', 'Non-Defective'});

4.2 Calculate Metrics

accuracy = sum(predLabels == trueLabels) / numel(trueLabels);
precision = confMat(2,2) / (confMat(2,2) + confMat(1,2)); % TP/(TP+FP)
recall = confMat(2,2) / (confMat(2,2) + confMat(2,1)); % TP/(TP+FN)
f1Score = 2 * (precision * recall) / (precision + recall);

fprintf('Accuracy: %.2f%%, Precision: %.2f, Recall: %.2f, F1-Score: %.2f\n', ...
    accuracy*100, precision, recall, f1Score);

4.3 Visualize Defects with Grad-CAM

Highlight defective regions using gradient-weighted class activation mapping:

img = imread('test_defect.jpg');
img = imresize(img, [224 224]);
[classLabel, score] = classify(defectDetector, img);

% Generate Grad-CAM
featureLayer = 'activation_49_relu'; % ResNet-50 intermediate layer
gradCAMMap = gradCAM(defectDetector, img, classLabel, 'FeatureLayer', featureLayer);

% Overlay heatmap on image
figure;
imshow(img);
hold on;
imagesc(gradCAMMap, 'AlphaData', 0.5);
colormap('jet');
title(sprintf('Predicted: %s (Score: %.2f)', classLabel, score));

5. Deployment

5.1 Export Model for Inference

% Save the trained model
save('defectDetector.mat', 'defectDetector');

% Generate C/C++ code (requires MATLAB Coder)
codegen classify -args {ones(224,224,3,'uint8')} -config:lib -report

5.2 Real-Time Defect Detection

Integrate with cameras on production lines using MATLAB Compiler SDK or ROS Toolbox.

Key Challenges & Solutions

Small Datasets: Use data augmentation and pretrained models.
Class Imbalance: Apply class weighting or oversample the defective class.
Subtle Defects: Use high-resolution images or attention mechanisms.
Lighting Variations: Normalize image intensity or use histogram equalization.

Example Results

Metric	Value
Accuracy	98.5%
Precision	97.2%
Recall	96.8%
F1-Score	97.0%

MATLAB Toolboxes

Deep Learning Toolbox: For CNN training and transfer learning.
Image Processing Toolbox: For preprocessing/augmentation.
Computer Vision Toolbox: For Grad-CAM and object detection.
MATLAB Coder: For deploying models to edge devices.

Advanced Techniques

Object Detection: Use YOLOv4 or Faster R-CNN to localize defects.

% Example: Train YOLOv4 for defect localization
detector = trainYOLOv4ObjectDetector(trainingData, resnet50, options);

Anomaly Detection: Use autoencoders for unsupervised defect detection.
Semantic Segmentation: Segment defects at pixel level using U-Net.

Final Notes

Label data efficiently using the Image Labeler app.
Use Experiment Manager to automate hyperparameter tuning.
Combine with IoT platforms for real-time monitoring in smart grids.

This workflow enables automated, high-accuracy defect detection in electrical components, reducing manual inspection costs and improving reliability.