Spark-based deep convolutional neural network optimization and its application to waste classification assessment

Abstract

With the increasingly serious environmental pollution problem, waste classification has become a key issue for global sustainable development. Traditional waste classification methods usually rely on manual processing or rule engines, which are difficult to cope with a large amount of complex waste image data, especially in efficient classification and real-time processing challenges. To solve this problem, this paper proposes a deep convolutional neural network optimization method based on the Spark distributed framework, aiming to achieve fine-grained processing of waste classification. The method combines migration learning with distributed computing, utilizing a ResNet-50 pre-trained model for feature extraction, and enhances feature capture of key objects in waste images. The experimental results show that although the current model shows good results in the waste classification task, it still suffers from insufficient accuracy in recognizing some categories, especially in the image classification task in complex backgrounds. Future research can further improve the accuracy and stability of the model by exploring more advanced network architectures, optimization strategies, and combining techniques such as the attention mechanism. Nevertheless, this study provides effective technical support for the waste classification system and promotes the intelligent and automated application of waste classification, but further improvement is still needed to adapt to more complex practical application scenarios.