EBiDNet: A Character Detection Algorithm for LCD Interfaces Based on an Improved DBNet Framework

• Published in: Symmetry (Basel) Vol. 17; no. 9; p. 1443
• Main Authors: Wang, Kun, Wu, Yinchuan, Yan, Zhengguo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 03.09.2025
• Subjects: Accuracy; Adaptability; Algorithms; Data integration; Deep learning; Efficiency; Feature extraction; LCDs; Liquid crystal displays; Localization; Machine learning; Machine vision; Process controls; Robustness; Semantics; Telecommunication systems
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym17091443

Characters on liquid crystal display (LCD) interfaces often appear densely arranged, with complex image backgrounds and significant variations in target appearance, posing considerable challenges for visual detection. To improve the accuracy and robustness of character detection, this paper proposes an enhanced character detection algorithm based on the DBNet framework, named EBiDNet (EfficientNetV2 and BiFPN Enhanced DBNet). This algorithm integrates machine vision with deep learning techniques and introduces the following architectural optimizations. It employs EfficientNetV2-S, a lightweight, high-performance backbone network, to enhance feature extraction capability. Meanwhile, a bidirectional feature pyramid network (BiFPN) is introduced. Its distinctive symmetric design ensures balanced feature propagation in both top-down and bottom-up directions, thereby enabling more efficient multiscale contextual information fusion. Experimental results demonstrate that, compared with the original DBNet, the proposed EBiDNet achieves a 9.13% increase in precision and a 14.17% improvement in F1-score, while reducing the number of parameters by 17.96%. In summary, the proposed framework maintains lightweight design while achieving high accuracy and strong robustness under complex conditions.