Dissertação de Mestrado

Real-Time Industrial Surface Defect Detection Based on Lightweight Deep Learning Network

Zhenyu Ge2026

Informações chave

Autores:

Zhenyu Ge (Zhenyu Ge)

Orientadores:

António José Castelo Branco Rodrigues (António José Castelo Branco Rodrigues); Piao Guanyu

Publicado em

July 31, 2026

Resumo

Industrial surface defect detection often requires both accurate localization and high inference speed, especially in real-time inspection systems.This thesis focuses on lightweight detection for small-scale industrial defects and weak-texture defects.These defects are often difficult to distinguish from complex backgrounds in industrial production ,which may lead to missed detections.Based on RTMDet-Tiny,this thesis designs a lightweight detection model with detail enhancement.The aim is to improve the model’s ability to represent the features of small-scale industrial defects and weak-texture defects.The framework combines three main components.First,P3 Detail Shortcut strengthens shallow high-resolution features before they are fused into the P3 layer,so that local texture and boundary cues can be better retained.Second,Head Lite simplifies the detection head to reduce redundant computation in the main detector.Finally,a Sparse Local Refiner is used for selected weak texture candidates,providing local verification and conservative box adjustment when the main detector gives uncertain predictions.Experiments are mainly conducted on NEU-DET,with additional validation on DeepPCB and MagneticTile.Compared with the controlled RTMDet-Tiny +CROP(0 .75) baseline ,the final pipeline improves mAP@0 .5 from 0.721 to 0.8107.In the controlled ablation route, the final main detector reduces parameters from 4.875M to 4.727M and FLOPs from 8.029G to 6.760G.The cross-dataset results further indicate that the proposed modules show transferability to other industrial defect scenarios,although the improvements remain category-dependent.Overall,the proposed framework provides a practical accuracy-complexity trade-off for lightweight industrial surface defect detection.

Detalhes da publicação

Autores da comunidade :

Orientadores desta instituição:

Domínio Científico (FOS)

electrical-engineering-electronic-engineering-information-engineering - Engenharia Eletrotécnica, Eletrónica e Informática

Idioma da publicação (código ISO)

por - Português

Acesso à publicação:

Acesso Embargado

Data do fim do embargo:

May 30, 2027

Nome da instituição

Instituto Superior Técnico