In addressing the prominent issues of climate change, resource scarcity, and environmental pollution associated with ho usehold waste, extensive research has been conducted on intelligent waste classification methods. These efforts range fro m traditional classification algorithms to machine learning and neural networks. However, challenges persist in effectively classifying waste in diverse environments and conditions due to insufficient datasets, increased complexity in neural netwo rk architectures, and performance limitations for real-world applications. Therefore, this paper proposes i-YOLOX as a sol ution for rapid classification and improved accuracy. The proposed model is evaluated based on network parameters, detect ion speed, and accuracy. To achieve this, a dataset comprising 10,000 samples of household waste, spanning 17 waste cate gories, is created. The i-YOLOX architecture is constructed by introducing the Involution channel convolution operator an d the Convolution Branch Attention Module (CBAM) into the YOLOX structure. A comparative analysis is conducted with the performance of the existing YOLO architecture. Experimental results demonstrate that i-YOLOX enhances the detectio n speed and accuracy of waste objects in complex scenes compared to conventional neural networks. This confirms the eff ectiveness of the proposed i-YOLOX architecture in the detection and classification of multiple household waste objects.