基于异构协同计算的智能垃圾分类系统设计OA

The global issue of"garbage encircling cities"is intensifying,making intelligent waste sorting a research hotspot for tackling this challenge.However,embedded platforms commonly face the trade-off dilemma of"limited computing power-high real-time require-ments-optimal recognition accuracy".The traditional approaches struggle to meet practical demands:cloud-based architectures suffer from high latency due to data transmission,pure embedded architectures lack sufficient computing power,and cloud-edge collaborative architectures still exhibit interaction delays.This paper proposes a heterogeneous collaborative computing architecture based on FPGA-STM32.The FPGA handles image preprocessing and parallel convolution computations,while the STM32 manages fully connected layer operations and classification decisions.Concurrently,a lightweight convolutional neural network is optimized through pruning into a"single convolution layer+three fully connected layers"structure,incorporating INT16 quantization and clipping mechanisms to bal-ance accuracy and hardware adaptability.The experiments demonstrate that the system achieves an 83.33%accuracy rate in identifying ten categories of household waste.Compared to the MATLAB platform,it accelerates inference by 15.675 times with a processing la-tency of only 40.004 ms.The low FPGA core resource utilization enables efficient deployment in embedded waste sorting scenarios such as communities and households.