This paper addresses the growing interest in deploying deep learning models directly in-sensor. We present "Q-Segment", a quantized real-time segmentation algorithm, and conduct a comprehensive evaluation on two low-power edge vision platforms, namely Sony IMX500, which has an in-sensors processor, and Sony Spresense, a low-power multi-core ARM Cortex-M microcontroller. One of the main goals of the model is to achieve end-to-end image segmentation for vessel-based medical diagnosis. Deployed on the IMX500 platform, Q-Segment achieves ultra-low inference time in-sensor of only 1.9 ms and energy consumption of only 5.7 mJ. We compare the proposed network with outperforming existing networks on various platforms by a factor of 75x (compared to ERFNet). The network architecture employs an encoder-decoder structure with skip connections, and results in a binary accuracy of 97.25% and an Area Under the Receiver Operating Characteristic Curve (AUC) of 96.97% on the CHASE dataset. This research contributes valuable insights into edge-based image segmentation, laying the foundation for efficient algorithms tailored to low-power environments.

翻译：本文针对将深度学习模型直接部署于传感器内的研究热点，提出了一种量化实时分割算法"Q-Segment"，并在两款低功耗边缘视觉平台——配备传感器内处理器的索尼IMX500与低功耗多核ARM Cortex-M微控制器索尼Spresense——上进行了全面评估。模型的核心目标之一是实现基于血管医学诊断的端到端图像分割。Q-Segment在IMX500平台部署后，传感器内推理时间仅需1.9毫秒，能耗仅为5.7毫焦耳。我们将所提网络与现有网络进行对比，在各平台上的性能超越幅度达75倍（相比ERFNet）。该网络架构采用带有跳跃连接的编码器-解码器结构，在CHASE数据集上实现了97.25%的二值分类准确率与96.97%的受试者工作特征曲线下面积（AUC）。本研究为基于边缘设备的图像分割提供了重要洞见，为适应低功耗环境的高效算法设计奠定了基础。