全球 “垃圾围城” 问题加剧,智能垃圾分类成为研究热点,但嵌入式平台普遍面临 “算力有限-实时性高-识别精度优” 的权衡困境。在传统方案中,云端架构依赖数据传输导致延迟高,纯嵌入式架构算力不足,云边协同架构仍存在交互延迟,均难以满足实际需求。文中提出基于 FPGA-STM32 的异构协同计算架构,FPGA 承担图像预处理与卷积并行计算,STM32负责全连接层运算与分类决策;同时优化轻量化卷积神经网络,经“单卷积层+三层全连接层”结构裁剪,引入INT16量化与钳位机制平衡精度与硬件适配性。实验结果表明,系统对10类生活垃圾的识别准确率达 83.33%,较MATLAB平台推理加速15.675倍,处理延时仅40.004 ms,FPGA核心资源占用率低,可高效部署于社区、家庭等嵌入式垃圾分类场景。

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 requirements-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 balance 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 latency of only 40.004 ms. The low FPGA core resource utilization enables efficient deployment in embedded waste sorting scenarios such as communities and households.