一個產品的成功與否與企業的成敗息息相關，而品質管理是影響產品成功的重要因素，也是形塑企業競爭力的重要環節。塑膠射出成型適用於生產形狀複雜的加工工藝，而使用普遍原因是因為塑膠射出成型能在較短的週期時間大量生產出產品，且具有非常高的產品精度，所以在消費型電子產品上被廣泛使用，其中因為產品需要非常精密的尺寸與外觀，使品質把關變得非常嚴格。基於完善的瑕疵檢測系統可以幫助產線更快的判定產品的好壞，藉以減少人力成本以及檢驗成本等動機與目的。本研究基於卷積神經網路，使用傳統卷積神經網路以及You Only Look Once (YOLO) V3做為本研究模型架構開發塑膠射出成型產品不良品瑕疵檢測系統。首先本研究選定Rombit所開發之工人安全與防疫追蹤穿戴裝置Romware ONE作為研究對象，拍攝一定數量的相片，經過預處理後將資料分別導入傳統卷積神經網路以及基於YOLO V3所開發模型中訓練，直到模型達到理想情況。本研究結果使用傳統卷積神經網路發生過度擬和情況，使實際使用不如預期。而YOLO V3建構之模型經過訓練與調整最終達到F1分數為0.9775，mAP50為96.8254，因此在偵測產品缺陷上面有良好表現。未來可能再細分更多缺陷類別，達到更精確的檢測效果。

The success of a product is closely related to the success or failure of the enterprise, and quality management is an important factor affecting the success of the product, but also an important link of shaping the competitiveness of enterprises. Plastic Injection Molding (PIM) is suitable for the production of complex shape machining process, and use the common reason for plastic injection molding can in a relatively short period of time the product in mass production and high precision products, so is widely used in consumer electronics, which because of the size and appearance of the products need to be very precise, make quality control is very strict. Based on the perfect defect detection system can help the production line to determine the quality of products more quickly, so as to reduce labor costs and inspection costs and other motives and objectives. This study based on convolutional neural network, uses traditional convolutional neural network and You Only Look Once (YOLO) V3 as the model framework to develop a defect detection system for plastic injection molding products. First, this research selects Romware ONE, a wearable device developed by Rombit, as the research object, and takes a certain number of photos. After preprocessing, the data is imported into the traditional convolutional neural network and the model developed based on YOLO V3 for training until the model reaches the ideal situation.
The result of this study is that the traditional convolutional neural networks are over-fitting and the actual use is not as expected. After training and adjustment, the model constructed by YOLO V3 finally achieved a F1 score of 0.9775 and a mAP50 score of 96.8254, so it has a good performance in detecting product defects. In the future, more defect categories may be subdivided to achieve more accurate detection results.

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), e00938.
Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. Paper presented at the 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey.
Aloysius, N., & Geetha, M. (2017). A review on deep convolutional neural networks. Paper presented at the 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India.
Benjdira, B., Khursheed, T., Koubaa, A., Ammar, A., & Ouni, K. (2019). Car detection using unmanned aerial vehicles: Comparison between faster r-cnn and yolov3. Paper presented at the 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), Muscat, Oman.
Bryce, D. M. (1997). Plastic injection molding: material selection and product design fundamentals. Dearborn, MI: Society of Manufacturing Engineers.
Choi, J., Chun, D., Kim, H., & Lee, H.-J. (2019). Gaussian yolov3: An accurate and fast object detector using localization uncertainty for autonomous driving. Paper presented at the Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Korea.
Coşkun, M., Uçar, A., Yildirim, Ö., & Demir, Y. (2017). Face recognition based on convolutional neural network. Paper presented at the 2017 International Conference on Modern Electrical and Energy Systems (MEES), Kremenchuk, Ukraine.
Gao, H., Zhang, Y., Zhou, X., & Li, D. (2018). Intelligent methods for the process parameter determination of plastic injection molding. Frontiers of Mechanical Engineering, 13(1), 85-95.
Gilbert, M. (2017). Plastics materials: Introduction and historical development. In Brydsons plastics materials. Oxford, Reino Unido: Elsevier (Butterworth-Heinemann).
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV.
Henderson, P., & Ferrari, V. (2016). End-to-end training of object class detectors for mean average precision. Paper presented at the Asian Conference on Computer Vision, Taipei, Taiwan.
Hosang, J., Benenson, R., & Schiele, B. (2017). Learning non-maximum suppression. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI.
Huang, R., Gu, J., Sun, X., Hou, Y., & Uddin, S. (2019). A rapid recognition method for electronic components based on the improved YOLO-V3 network. Electronics, 8(8), 825.
Huang, R., Pedoeem, J., & Chen, C. (2018). YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. Paper presented at the 2018 IEEE International Conference on Big Data, Seattle, WA.
Huang, Y.-Q., Zheng, J.-C., Sun, S.-D., Yang, C.-F., & Liu, J. (2020). Optimized YOLOv3 algorithm and its application in traffic flow detections. Applied Sciences, 10(9), 3079.
Imoto, K., Nakai, T., Ike, T., Haruki, K., & Sato, Y. (2018). A CNN-based transfer learning method for defect classification in semiconductor manufacturing. Paper presented at the 2018 International Symposium on Semiconductor Manufacturing (ISSM), Tokyo, Japan.
Javidi, B. (2002). Image recognition and classification: algorithms, systems, and applications. Boca Raton, FL: CRC press.
Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review, 53(8), 5455-5516.
Kim, J.-Y., & Cho, S.-B. (2021). Deep CNN Transferred from VAE and GAN for classifying irritating noise in automobile. Neurocomputing, 452, 395-403.
Kim, S., Kim, W., Noh, Y.-K., & Park, F. C. (2017). Transfer learning for automated optical inspection. Paper presented at the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, Alaska.
Kitayama, S., Yokoyama, M., Takano, M., & Aiba, S. (2017). Multi-objective optimization of variable packing pressure profile and process parameters in plastic injection molding for minimizing warpage and cycle time. The International Journal of Advanced Manufacturing Technology, 92(9), 3991-3999.
Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., & Shi, J. (2020). Foveabox: Beyound anchor-based object detection. IEEE Transactions on Image Processing, 29, 7389-7398.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097-1105.
Kuznetsova, A., Maleva, T., & Soloviev, V. (2020). Using YOLOv3 algorithm with pre-and post-processing for apple detection in fruit-harvesting robot. Agronomy, 10(7), 1016.
Kwak, K., Kim, W., & Kim, K. (2018). Latecomer firms combination of strategies in a specialized suppliers sector: A comparative case study of the Korean plastic injection molding machine industry. Technological Forecasting and Social Change, 133, 190-205.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
Li, C., Wang, R., Li, J., & Fei, L. (2020). Face detection based on YOLOv3. Recent Trends in Intelligent Computing, Communication and Devices (pp. 277-284). Singapore: Springer.
Li, J., Gu, J., Huang, Z., & Wen, J. (2019). Application research of improved YOLO V3 algorithm in PCB electronic component detection. Applied Sciences, 9(18), 3750.
Liao, H.-C., Lim, Z.-Y., Hu, Y.-X., & Tseng, H.-W. (2018). Guidelines of automated optical inspection (AOI) system development. Paper presented at the 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP), Shenzhen, China.
Lim, D.-u., Kim, Y.-G., & Park, T.-H. (2019). SMD classification for automated optical inspection machine using convolution neural network. Paper presented at the 2019 Third IEEE International Conference on Robotic Computing (IRC), Naples, Italy.
Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI.
Liu, G., Nouaze, J. C., Touko Mbouembe, P. L., & Kim, J. H. (2020). YOLO-tomato: A robust algorithm for tomato detection based on YOLOv3. Sensors, 20(7), 2145.
Ma, H., Liu, Y., Ren, Y., & Yu, J. (2020). Detection of collapsed buildings in post-earthquake remote sensing images based on the improved YOLOv3. Remote Sensing, 12(1), 44.
Macedo, C., Freitas, C., Brito, A. M., Santos, G., Faria, L., Laranjeira, J., & Simoes, R. (2019). Influence of dynamic temperature control on the injection molding process of plastic components. Procedia Manufacturing, 38, 1338-1346.
Marini, F., Bucci, R., Magrì, A., & Magrì, A. (2008). Artificial neural networks in chemometrics: History, examples and perspectives. Microchemical Journal, 88(2), 178-185.
Mathur, A., Dangayach, G., Mittal, M., & Sharma, M. K. (2011). Performance measurement in automated manufacturing. Measuring Business Excellence, 15(1), 77-91.
Medus, L. D., Saban, M., Francés-Víllora, J. V., Bataller-Mompeán, M., & Rosado-Muñoz, A. (2021). Hyperspectral image classification using CNN: Application to industrial food packaging. Food Control, 125, 107962.
Moctezuma, L. E. G., Jokinen, J., Postelnicu, C., & Lastra, J. L. M. (2012). Retrofitting a factory automation system to address market needs and societal changes. Paper presented at the IEEE 10th International Conference on Industrial Informatics, Beijing, China.
Park, J.-H., Hwang, H.-W., Moon, J.-H., Yu, Y., Kim, H., Her, S.-B., . . . Lee, S.-J. (2019). Automated identification of cephalometric landmarks: Part 1—Comparisons between the latest deep-learning methods YOLOV3 and SSD. The Angle Orthodontist, 89(6), 903-909.
Punn, N. S., Sonbhadra, S. K., Agarwal, S., & Rai, G. (2020). Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques. arXiv:2005.01385.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV.
Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv:1804.02767.
Rosato, D. V., & Rosato, M. G. (2000). Injection molding handbook. New York, NY: Springer Science & Business Media.
Rosli, M., Ishak, M. I., Jamalludin, M. R., Khor, C., Nawi, M., & Syafiq, A. M. (2019). Simulation-based optimization of plastic injection molding parameter for aircraft part fabrication using response surface methodology (RSM). Paper presented at the IOP Conference Series: Materials Science and Engineering, Bangkok, Thailand.
Sadeghi, B. (2000). A BP-neural network predictor model for plastic injection molding process. Journal of Materials Processing Technology, 103(3), 411-416.
Saif, N., Ahmmed, N., Pasha, S., Shahrin, M. S. K., Hasan, M. M., Islam, S., & Jameel, A. S. M. M. (2019). Automatic License Plate Recognition System for Bangla License Plates using Convolutional Neural Network. Paper presented at the TENCON 2019-2019 IEEE Region 10 Conference (TENCON), Kerala, India.
Sanghvi, K., Shah, A., Shah, P., Shah, P., & Rathod, S. S. (2021). Smart Parking Solutions for On-Street and Off-Street Parking. Paper presented at the 2021 International Conference on Communication Information and Computing Technology (ICCICT), Mumbai, India.
Shang, L., Yang, Q., Wang, J., Li, S., & Lei, W. (2018). Detection of rail surface defects based on CNN image recognition and classification. Paper presented at the 2018 20th International Conference on Advanced Communication Technology (ICACT), Chuncheon, South Korea.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... Rabinovich, A. (2015). Going deeper with convolutions. Paper presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA.
Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., & Liang, Z. (2019). Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Computers and Electronics in Agriculture, 157, 417-426.
Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International Journal of Computer Vision, 104(2), 154-171.
Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0–a glimpse. Procedia Manufacturing, 20, 233-238.
Wang, S.-H., Muhammad, K., Hong, J., Sangaiah, A. K., & Zhang, Y.-D. (2020). Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Computing and Applications, 32(3), 665-680.
Wu, Y.-c., & Feng, J.-w. (2018). Development and application of artificial neural network. Wireless Personal Communications, 102(2), 1645-1656.
Yang, Y., Pan, L., Ma, J., Yang, R., Zhu, Y., Yang, Y., & Zhang, L. (2020). A high-performance deep learning algorithm for the automated optical inspection of laser welding. Applied Sciences, 10(3), 933.
Zhiqiang, W., & Jun, L. (2017). A review of object detection based on convolutional neural network. Paper presented at the Proceedings of the 2017 36th Chinese Control Conference (CCC), Dalian, China.
Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object detection in 20 years: A survey. arXiv:1905.05055.