<![CDATA[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.]]>
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