<![CDATA[Rapid globalization has led to the growth of a significant amount of waste, including in the industry. Due to the considerable amount of growth of waste every year, effective and efficient waste management is needed to protect our environment. In the leather product industry, waste management is strongly important since it may have a significant impact to the employee and production process. Regarding those issues, waste management technology is considered proposed in order to solve those problems. Current research reported the outstanding work of implementing artificial intelligence for detecting and recognizing industrial waste. Artificial intelligence was proven to be a highly recommended approach that is able to classify several waste types with outstanding performance. Regardless of those facts, artificial intelligence still remains several hurdles, such as the high computational demands, especially for deep learning networks. Regarding the mentioned issue, we proposed a more proper deep learning network for recognizing industrial waste. In this research work, we use Single Shot Detector (SSD) to recognize and classify industrial waste. Our proposed solution was performed in the TrashNet dataset and Waste Picture dataset. Our proposed solution successfully achieve mAP of 0.8813, accuracy of 0,9795, precision of 0,9985 and recall of 0,9693 in the training process. Whereas, in the testing process we achieve average accuracy of 0,8254. According to those results, we can conclude that our proposed solution is suitable for industrial waste detection and has potential to be implemented as an embedded system for recognizing industrial waste automatically.]]>
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