# MRC-DETR: An Adaptive Multi-Residual Coupled Transformer for Bare Board PCB Defect Detection

arXiv: https://arxiv.org/abs/2507.03386 · HTML: https://mlsanity.com/paper/2507.03386/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 54 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 29 of 29)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR](https://mlsanity.com/paper/2201.12329/) (2022)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [DN-DETR: Accelerate DETR Training by Introducing Query DeNoising](https://mlsanity.com/paper/2203.01305/) (2022)
- [Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks](https://mlsanity.com/paper/2303.03667/) (2023)
- [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://mlsanity.com/paper/2410.13842/) (2024)
- [DEIM: DETR with Improved Matching for Fast Convergence](https://mlsanity.com/paper/2412.04234/) (2024)
- [Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration](https://mlsanity.com/paper/2408.15994/) (2024)
- [Poly Kernel Inception Network for Remote Sensing Detection](https://mlsanity.com/paper/2403.06258/) (2024)
- [Efficient Multi-Scale Attention Module with Cross-Spatial Learning](https://mlsanity.com/paper/2305.13563/) (2023)
- [Masked Autoencoders Enable Efficient Knowledge Distillers](https://mlsanity.com/paper/2208.12256/) (2022)
- [Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks](https://mlsanity.com/paper/1905.09646/) (2019)
- [Online PCB Defect Detector On A New PCB Defect Dataset](https://mlsanity.com/paper/1902.06197/) (2019)
- [Scale-Equalizing Pyramid Convolution for Object Detection](https://mlsanity.com/paper/2005.03101/) (2020)
- [UniUIR: Considering Underwater Image Restoration as An All-in-One Learner](https://mlsanity.com/paper/2501.12981/) (2025)
- [Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases](https://mlsanity.com/paper/2401.00926/) (2024)
- [MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning](https://mlsanity.com/paper/2404.05621/) (2024)
- [IPG-Net: Image Pyramid Guidance Network for Small Object Detection](https://mlsanity.com/paper/1912.00632/) (2019)
- [ELA: Efficient Local Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/2403.01123/) (2024)
- [LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction](https://mlsanity.com/paper/2409.17759/) (2024)

## Built on by (most cited first, 2 of 2)

- [UniPCB: A Unified Vision-Language Benchmark for Open-Ended PCB Quality Inspection](https://mlsanity.com/paper/2601.19222/) (2026)
- [UniPCB: A Generation-Assisted Vision-Based Measurement Framework for PCB Defect Inspection](https://mlsanity.com/paper/2605.04635/) (2026)

## Abstract (arXiv)

In modern electronic manufacturing, defect detection on Printed Circuit Boards (PCBs) plays a critical role in ensuring product yield and maintaining the reliability of downstream assembly processes. However, existing methods often suffer from limited feature representation, computational redundancy, and insufficient availability of high-quality training data -- challenges that hinder their ability to meet industrial demands for both accuracy and efficiency. To address these limitations, we propose MRC-DETR, a novel and efficient detection framework tailored for bare PCB defect inspection, built upon the foundation of RT-DETR. Firstly, to enhance feature representation capability, we design a Multi-Residual Directional Coupled Block (MRDCB). This module improves channel-wise feature interaction through a multi-residual structure. Moreover, a cross-spatial learning strategy is integrated to capture fine-grained pixel-level relationships, further enriching the representational power of the extracted features. Secondly, to reduce computational redundancy caused by inefficient cross-layer information fusion, we introduce an Adaptive Screening Pyramid Network (ASPN). This component dynamically filters and aggregates salient low-level features, selectively fusing them with high-level semantic features. By focusing on informative regions and suppressing redundant computations, ASPN significantly improves both efficiency and detection accuracy. Finally, to tackle the issue of insufficient training data, particularly in the context of bare PCBs, we construct a new, high-quality dataset that fills a critical gap in current public resources. Our dataset not only supports the training and evaluation of our proposed framework but also serves as a valuable benchmark for future research in this domain.