A Review of Neural Network Approach on Engineering Drawing Recognition and Future Directions

Muhammad Syukri Mohd Yazed - Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia
Ezak Fadzrin Ahmad Shaubari - Universiti Tun Hussein Onn Malaysia, Batu Pahat, Johor, Malaysia
Moi Hoon Yap - bManchester Metropolitan University, Manchester, M15 6BH, United Kingdom

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.7.4.01716


Engineering Drawing (ED) digitization is a crucial aspect of modern industrial processes, enabling efficient data management and facilitating automation. However, the accurate detection and recognition of ED elements pose significant challenges. This paper presents a comprehensive review of existing research on ED element detection and recognition, focusing on the role of neural networks in improving the analysis process. The study evaluates the performance of the YOLOv7 model in detecting ED elements through rigorous experimentation. The results indicate promising precision and recall rates of up to 87.6% and 74.4%, respectively, with a mean average precision (mAP) of 61.1% at IoU threshold 0.5. Despite these advancements, achieving 100% accuracy remains elusive due to factors such as symbol and text overlapping, limited dataset sizes, and variations in ED formats. Overcoming these challenges is vital to ensuring the reliability and practical applicability of ED digitization solutions. By comparing the YOLOv7 results with previous research, the study underscores the efficacy of neural network-based approaches in handling ED element detection tasks. However, further investigation is necessary to address the challenges above effectively. Future research directions include exploring ensemble methods to improve detection accuracy, fine-tuning model parameters to enhance performance, and incorporating domain adaptation techniques to adapt models to specific ED formats and domains. To enhance the real-world viability of ED digitization solutions, this work highlights the importance of conducting testing on diverse datasets representing different industries and applications. Additionally, fostering collaborations between academia and industry will enable the development of tailored solutions that meet specific industrial needs. Overall, this research contributes to understanding the challenges in ED digitization and paves the way for future advancements in this critical field.


Engineering drawings; ED analysis; Neural network; Object detection and recognition; Industrial practice.

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