A 1 millisecond (1-ms) vision system that guarantees high efficiency and timely response for tomato defect detection is essential for factory automation. Because of various defect appearances, recently many existing researches focus on CNN based defect detection, but few of them attempt to reach high processing speed to adapt to the factorial assembly line. This paper proposes a global to multi-scale local based parallel architecture with hardwired CNN for tomato defect detection. This architecture breaks down image-wise detection into pixel-wise localization and block-wise classification. The pixel-wise localization utilizes tomato-aware information as constraints for localization performance. The block-wise classification uses a fully pipelined network structure to obtain the classification result for each block as the pixel stream moves through the network. The classification network has a six-layer lightweight network structure with quantization for hardwired type implementation on FPGA. The experiment results show that the proposed architecture processes 1000 FPS images with 0.9476 ms/frame delay. And for detection performance, this architecture keeps at 80.18%, only 1.31% lower than ResNet50 based detection system.

中文翻译：

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具有硬连线 CNN 的全局到多尺度局部架构，用于 1 毫秒番茄缺陷检测

1 毫秒 (1-ms) 视觉系统可保证番茄缺陷检测的高效率和及时响应，对于工厂自动化至关重要。由于各种缺陷的出现，最近许多现有的研究都集中在基于CNN的缺陷检测上，但很少有人尝试达到高处理速度以适应阶乘装配线。本文提出了一种基于全局到多尺度局部的并行架构，具有硬连线 CNN，用于番茄缺陷检测。该架构将图像检测分解为像素定位和块分类。像素级定位利用番茄感知信息作为定位性能的约束。逐块分类使用完全流水线的网络结构，当像素流在网络中移动时获得每个块的分类结果。该分类网络具有六层轻量级网络结构，具有量化功能，可在 FPGA 上进行硬连线类型实现。实验结果表明，所提出的架构以 0.9476 ms/帧延迟处理 1000 FPS 图像。对于检测性能，该架构保持了为 80.18%，仅比基于 ResNet50 的检测系统低 1.31%。