# SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection

arXiv: https://arxiv.org/abs/2207.00589 · HTML: https://mlsanity.com/paper/2207.00589/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 3 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [TGRNet: A Table Graph Reconstruction Network for Table Structure Recognition](https://mlsanity.com/paper/2106.10598/) (2021)
- [MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection](https://mlsanity.com/paper/1909.04247/) (2019)
- [Deformable Kernel Convolutional Network for Video Extreme Super-Resolution](https://mlsanity.com/paper/2010.00154/) (2020)

## Built on by (most cited first, 3 of 3)

- [Less yet robust: crucial region selection for scene recognition](https://mlsanity.com/paper/2409.14741/) (2024)
- [Unsupervised Multi-Attention Meta Transformer for Rotating Machinery Fault Diagnosis](https://mlsanity.com/paper/2509.09251/) (2025)
- [UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting](https://mlsanity.com/paper/2604.02905/) (2026)

## Abstract (arXiv)

The quality of industrial components is critical to the production of special equipment such as robots. Defect inspection of these components is an efficient way to ensure quality. In this paper, we propose a hybrid network, SSD-Faster Net, for industrial defect inspection of rails, insulators, commutators etc. SSD-Faster Net is a two-stage network, including SSD for quickly locating defective blocks, and an improved Faster R-CNN for defect segmentation. For the former, we propose a novel slice localization mechanism to help SSD scan quickly. The second stage is based on improved Faster R-CNN, using FPN, deformable kernel(DK) to enhance representation ability. It fuses multi-scale information, and self-adapts the receptive field. We also propose a novel loss function and use ROI Align to improve accuracy. Experiments show that our SSD-Faster Net achieves an average accuracy of 84.03%, which is 13.42% higher than the nearest competitor based on Faster R-CNN, 4.14% better than GAN-based methods, more than 10% higher than that of DNN-based detectors. And the computing speed is improved by nearly 7%, which proves its robustness and superior performance.