Real-time defect detection of TFT-LCD displays using a lightweight network architecture

Chen, Ping; Chen, Mingfang; Wang, Sen; Song, Yanjin; Cui, Yu; Chen, Zhongping; Zhang, Yongxia; Chen, Songlin; Mo, Xiang

doi:10.1007/s10845-023-02110-7

Cited by 3 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep-learning-based object detection methods no longer require handcrafted features but instead learn relevant features directly through training. The detection performance improves with increasing amounts of data, leading to successful applications in various fields [11]. Dimitriou et al [12] introduced a model based on a three-dimensional convolutional neural network capable of identifying defects on wafers.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network Defect Detection Algorithm for Wire Bonding X-ray Images

Zhan,

Huang,

et al. 2023

Micromachines

View full text Add to dashboard Cite

To address the challenges of complex backgrounds, small defect sizes, and diverse defect types in defect detection of wire bonding X-ray images, this paper proposes a convolutional-neural-network-based defect detection method called YOLO-CSS. This method designs a novel feature extraction network that effectively captures semantic features from different gradient information. It utilizes a self-adaptive weighted multi-scale feature fusion module called SMA which adaptively weights the contribution of detection results based on different scales of feature maps. Simultaneously, skip connections are employed at the bottleneck of the network to ensure the integrity of feature information. Experimental results demonstrate that on the wire bonding X-ray defect image dataset, the proposed algorithm achieves mAP 0.5 and mAP 0.5–0.95 values of 97.3% and 72.1%, respectively, surpassing the YOLO series algorithms. It also exhibits certain advantages in terms of model size and detection speed, effectively balancing detection accuracy and speed.

show abstract

Section: Introductionmentioning

confidence: 99%