LightDefectNet-18: A Lightweight Framework for Multi-Domain Defect Detection

doi:10.1007/s10921-025-01327-1

LightDefectNet-18: A Lightweight Framework for Multi-Domain Defect Detection

2026-01-11

0

PRE

AI

A

Ajantha Vijayakumar

K

K. Joseph Abraham Sundar *

M

Muthaiah Rajappa

R

Ramakrishnan Sundaram

DOI：10.1007/s10921-025-01327-1

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Detection of defects in diverse domains requires a specialized object detection system capable of identifying various types of flaws of different sizes and shapes. This research addresses detection challenges across six critical domains: saline bottle level monitoring, screw defect detection, magnetic tile inspection, road crack analysis, fabric flaw identification, and potato leaf disease recognition. These applications exhibit unique visual characteristics, including variable defect morphologies, subtle texture variations, and domain-specific features, which conventional detection models often fail to adequately address. Standard Faster R-CNN implementations with ResNet-50 and VGG-16 backbones offer general feature extraction but lack domain-specific optimization for these specialized applications. We propose LightDefectNet-18, a custom CNN backbone for Faster R-CNN featuring dual residual blocks with skip connections, strategic kernel sizing, and progressive channel expansion, integrated with a Feature Pyramid Network (FPN) architecture. The FPN component creates a multi-scale feature hierarchy through top-down pathways and lateral connections, effectively detecting defects across scales. The architecture incorporates batch normalization layers, calibrated dropout, and proper weight initialization to enhance feature preservation and gradient flow. When integrated with Faster R-CNN, we implement refined anchor configurations optimized for multi-scale defect detection across our target applications, with tailored anchor sizes and aspect ratios for each pyramid level. The detection pipeline employs an adaptive optimization strategy with learning rate scheduling and early stopping mechanisms. The quantitative evaluation demonstrates superior detection performance across all target applications compared to standard backbones, with significant improvements in Average Precision using a relaxed IoU threshold specifically calibrated for industrial defect detection scenarios. The model's FPN-enhanced architecture effectively addresses the challenges of capturing fine-grained visual features essential for distinguishing subtle anomalies at multiple scales in specialized materials while maintaining computational efficiency suitable for deployment in real-world industrial and agricultural monitoring systems, even with limited training data.

Keyword:

Defect detection

CNN

Industry 4.0

Deep learning

Faster R-CNN

FPN

LightDefectNet-18: A Lightweight Framework for Multi-Domain Defect Detection

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