Development of Automatic Industrial Waste Detection System for Leather Products using Artificial Intelligence


  • Lista Meria Univerity of Esa Unggul



Artificial Intelligence, Deep Learning Networks, Detecting Waste of Economic Activity


This rapid world situation led to a significant growth in the amount of waste, including in economic activity. Due to the huge amount of waste growth every year, effective and efficient waste management is needed to protect our environment. In leather products economic activities, waste management is very important because it can have a significant effect on labor and manufacturing processes. Given the problem, waste management technology is considered the answer to solve the situation. Current investigations report remarkable results from the sealing of artificial intelligence-based tools that serve to detect and acknowledge whether it is an industrial waste. This artificial intelligence-based tool proved to be the answer to this situation approach capable of grouping several types of garbage with good performance. Despite these advantages, this artificial intelligence-based tool still finds some limitations, such as high computing demands, especially for deep learning networks. In view of these constraints, we propose a deeper learning network that is more appropriate for recognizing the waste of economic activity. In the course of this investigation, we used a Single Shot Detector to acknowledge and classify economic activity waste. The proposed completion ideas are carried out in the TrashNet dataset and the Waste Picture dataset. Our solution achieved an mAP of 0.8813, accuracy of 0.9795, performance measure of 0.9985 and conformance of 0.9693 in the training process. Meanwhile, in the process of verifying the tool, we achieved an average accuracy of 0.8254. Based on these experiments, we can conclude that our solution is suitable for detecting waste of economic activity and has the opportunity to be implemented as an installed system for programmatically recognizing waste of economic activity.


D'Angelo, V., Cappa, F., & Peruffo, E. (2022). Green manufacturing for sustainable development: The positive effects of green activities, green investments, and non‐green products on economic performance. Business Strategy and the Environment.

Tharmia, S., Nilawar, R., Aswale, N., Kumari, S., & Gupta, P. (2022, August). Waste Management and Wealth Generation Through Waste Elimination. In 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT) (pp. 674-680). IEEE.

Umar, U. A., Shafiq, N., & Ahmad, F. A. (2021). A case study on the effective implementation of the reuse and recycling of construction & demolition waste management practices in Malaysia. Ain Shams engineering journal, 12(1), 283-291.

Pardini, K., Rodrigues, JJ, Diallo, O., Das, AK, de Albuquerque, VHC, & Kozlov, S.A. (2020). Smart waste management solutions aimed at citizens. Sensors , 20 (8), 2380.

Sriliasta, C., Wuisan, D. S. S., & Mariyanti, T. (2022). Functions of Artificial Intelligence, Income Investment Instrument, and Crypto Money in Era of The Fourth Revolution. International Transactions on Artificial Intelligence, 1(1), 117-128.

Wamba-Taguimdje, S. L., Fosso Wamba, S., Kala Kamdjoug, J. R., & Tchatchouang Wanko, C. E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924.

Chidepatil, A., Bindra, P., Kulkarni, D., Qazi, M., Kshirsagar, M., & Sankaran, K. (2020). From waste to cash: how can blockchain and multi-sensor-driven artificial intelligence transform the circular economy of plastic waste?. Administrative Sciences , 10 (2), 23.

Tharani, M., Amin, A. W., Maaz, M., & Taj, M. (2020). Attention neural network for trash detection on water channels. arXiv preprint arXiv:2007.04639.

Mao, WL, Chen, WC, Wang, CT, & Lin, YH (2021). Classification of recycled waste using an optimized convolutional neural network. Resources, Conservation and Recycling , 164 , 105132.

Saabith, S., Vinothraj, T., & Fareez, M. (2021). A review on Python libraries and Ides for Data Science. Int. J. Res. Eng. Sci., 9(11), 36-53.

Mezzina, G., & De Venuto, D. (2021, June). Combination of RGB and 3D segmentation data for autonomous object manipulation in personal care robotics. In 2021 the 16th International Conference on Integrated Systems Design & Technology in the Era of Nanoscale (DTIS) (pp. 1-6). IEEE.

Lester, J. N., Cho, Y., & Lochmiller, C. R. (2020). Learning to do qualitative data analysis: A starting point. Human Resource Development Review, 19(1), 94-106.

Jose, J., Gautam, N., Tiwari, M., Tiwari, T., Suresh, A., Sundararaj, V., & Rejeesh, MR (2021). The image quality improvement scheme uses a youth identity search algorithm in the NSST domain for multimodal medical image fusion. Biomedical Signal Processing and Control, 66, 102480.

Kumar, A., & Srivastava, S. (2020). An object detection system based on a convolution neural network using a single shot multi-box detector. Procedia Computer Science, 171, 2610-2617.

Li, J., Cheng, B., Feris, R., Xiong, J., Huang, TS, Hwu, WM, & Shi, H. (2021). Pseudo-IoU: Improves label assignment in securing anchor-free objects. In the Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition (pp.2378-2387).

Panwar, H., Gupta, PK, Siddiqui, MK, Morales-Menendez, R., Bhardwaj, P., Sharma, S., & Sarker, IH (2020). AquaVision: Automate the detection of waste in water bodies using deep learning transfer. Chemical and Environmental Engineering Case Studies, 2, 100026.

Littlejohns, T.J., Holliday, J., Gibson, L.M., Garratt, S., Oesingmann, N., Alfaro-Almagro, F., ... & Allen, N.E. (2020). Improved UK Biobank imaging of 100,000 participants: thinking, data collection, management and future directions. Nature communications, 11(1), 2624.

Shuai, Q., & Wu, X. (2020, October). Object detection system based on SSD algorithm. At the 2020 international Conference on culture-oriented science & technology (ICCST) (pp.141-144). IEEE.

Peharz, R., Vergari, A., Stelzner, K., Molina, A., Shao, X., Trapp, M., ... & Ghahramani, Z. (2020, August). Random product sum networks: A simple and effective approach to probabilistic deep learning. In Uncertainty in Artificial Intelligence (pp. 334-344). PMLR.

Iyer, R., Ringe, PS, & Bhensdadiya, KP (2021). Compare YOLOv3, YOLOv5s, and MobileNet-SSD V2 for real-time mask detection. Arctic. int. J.Res. Eng. Technol , 8 , 1156-1160.

Lukita, C., Suwandi, S., Harahap, EP, Rahardja, U., & Nas, C. (2020). Curriculum 4.0: adoption of the industrial era 4.0 as an assessment of the quality of higher education. IJCCS (Journal of Computing Systems and Cybernetics Indonesia), 14 (3), 297-308.