Design of Object Detection System Using Deep Learning in Laboratory Room: Perancangan Sistem Deteksi Objek Menggunakan Yolov5 Pada Ruangan Laboratorium

Dion Omong; Dwisnanto Putro; Jane Litouw

doi:10.35793/jtek.v14i1.54953

Authors

Dion Omong Universitas Sam Ratulangi
M. Dwisnanto Putro Universitas Sam Ratulangi
Jane Litouw Universitas Sam Ratulangi

DOI:

https://doi.org/10.35793/jtek.v14i1.54953

Keywords:

object detection, service robot, convolutional neural networks, You Only Look Once version 5

Abstract

Abstract — In today's era, the challenge of object detection within laboratory spaces is paramount. The need for automated monitoring systems supporting object detection within laboratory settings, particularly for service robots, is crucial for scientific research and safety. Utilizing convolutional artificial neural networks has shown promising capabilities in object recognition. This paper proposes a novel method for detecting and localizing specific objects within laboratory environments using deep learning techniques. Employing a lightweight primary feature extractor allows for the identification of crucial object features without imposing excessive computational demands. Additionally, a depthwise separable convolution module is introduced, designed to capture essential components within multi-level convolutional patches specific to laboratory conditions. To address the challenges encountered within laboratory spaces, a new dataset is proposed, encompassing issues such as lighting variations, blurriness, occlusions, and complex backgrounds. Evaluation results indicate that the proposed model surpasses other lightweight object detection models in laboratory settings, offering high accuracy and efficiency. This model can be efficiently implemented on low-tier devices, ensuring real-time object detection and processing within laboratory environments.

Keywords — object detection, service robot, convolutional neural networks, You Only Look Once version 5.

Abstrak — Di era sekarang ini, tantangan deteksi objek di dalam ruang laboratorium menjadi sangat penting. Kebutuhan akan sistem pemantauan otomatis yang mendukung deteksi objek di dalam laboratorium, khususnya untuk robot servis, sangat penting untuk penelitian ilmiah dan keselamatan. Memanfaatkan jaringan saraf tiruan convolutional telah menunjukkan kemampuan yang menjanjikan dalam pengenalan objek. Makalah ini mengusulkan metode baru untuk mendeteksi dan melokalisasi objek tertentu di dalam lingkungan laboratorium menggunakan teknik pembelajaran mendalam. Menggunakan ekstraktor fitur utama yang ringan memungkinkan identifikasi fitur objek yang penting tanpa membebani komputasi yang berlebihan. Selain itu, modul konvolusi yang dapat dipisahkan secara mendalam juga diperkenalkan, yang dirancang untuk menangkap komponen penting dalam patch konvolusi multi-level yang spesifik untuk kondisi laboratorium. Untuk mengatasi tantangan yang dihadapi di dalam ruang laboratorium, dataset baru diusulkan, yang mencakup masalah seperti variasi pencahayaan, kekaburan, oklusi, dan latar belakang yang kompleks. Hasil evaluasi menunjukkan bahwa model yang diusulkan melampaui model pendeteksian objek ringan lainnya di lingkungan laboratorium, menawarkan akurasi dan efisiensi yang tinggi. Model ini dapat diimplementasikan secara efisien pada perangkat tingkat rendah, memastikan pendeteksian dan pemrosesan objek secara real-time di lingkungan laboratorium.

Kata kunci — deteksi objek, robot layanan, jaringan saraf konvolusi, Anda Hanya Melihat Sekali versi 5.

References

L. Ali, F. Alnajjar, M. M. A. Parambil, M. I. Younes, Z. I. Abdelhalim, and H. Aljassmi, “Development of YOLOv5-Based Real-Time Smart Monitoring System for Increasing Lab Safety Awareness in Educational Institutions.,” Sensors (Basel)., vol. 22, no. 22, Nov. 2022, doi: 10.3390/s22228820.

J. Wang et al., “Research on Improved YOLOv5 for Low-Light Environment Object Detection,” Electronics, vol. 12, no. 14. 2023. doi: 10.3390/electronics12143089.

E. M. Ditria, S. Lopez-Marcano, M. Sievers, E. L. Jinks, C. J. Brown, and R. M. Connolly, “Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology With Deep Learning ,” Frontiers in Marine Science , vol. 7. 2020. [Online]. Available: https://www.frontiersin.org/articles/10.3389/fmars.2020.00429

H. Leo, F. Arnia, and K. Munadi, “Fine Tuning CNN Pre-trained Model Based on Thermal Imaging for Obesity Early Detection,” J. Rekayasa Elektr., vol. 18, Apr. 2022, doi: 10.17529/jre.v18i1.25100.

K. O. P. P. Nugraha and A. P. Rifai, “Convolutional Neural Network for Identification of Personal Protective Equipment Usage Compliance in Manufacturing Laboratory,” J. Ilm. Tek. Ind., vol. 22, no. 1, pp. 11–24, 2023, doi: 10.23917/jiti.v22i1.21826.

L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data, vol. 8, no. 1, p. 53, 2021, doi: 10.1186/s40537-021-00444-8.

Q. Hou, “Coordinate Attention for Efficient Mobile Network Design”.

K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8691 LNCS, no. PART 3, pp. 346–361, 2014, doi: 10.1007/978-3-319-10578-9_23.

Z. Li, “Road Aerial Object Detection Based on Improved YOLOv5,” J. Phys. Conf. Ser., vol. 2171, no. 1, 2022, doi: 10.1088/1742-6596/2171/1/012039.

S. Jalal, H. Ahmed, and M. Ahmed, Design a Robust Real-Time Trash Detection System Using YOLOv5 Variants. 2023. doi: 10.1109/GlobConET56651.2023.10149899.

W. Qiao, H. Guo, and E. Huang, “2023-Real-time Detection of Slug Flow in Subsea Pipelines by Embedding Yolo Object Detection Algorithm into Jetson Nano,” J. Mar. Sci. Eng., vol. 11, Aug. 2023, doi: 10.3390/jmse11091658.

Y. Li, L. He, M. Zhang, Z. Cheng, W. Liu, and Z. Wu, “Improving the Performance of the Single Shot Multibox Detector for Steel Surface Defects with Context Fusion and Feature Refinement,” Electronics, vol. 12, p. 2440, May 2023, doi: 10.3390/electronics12112440.

T. Shi, Y. Ding, and W. Zhu, “YOLOv5s_2E: Improved YOLOv5s for Aerial Small Target Detection,” IEEE Access, vol. PP, p. 1, Jan. 2023, doi: 10.1109/ACCESS.2023.3300372.