Image Signal Processors (ISPs) play important roles in image recognition tasks as well as in the perceptual quality of captured images. In most cases, experts make a lot of effort to manually tune many parameters of ISPs, but the parameters are sub-optimal. In the literature, two types of techniques have been actively studied: a machine learning-based parameter tuning technique and a DNN-based ISP technique. The former is lightweight but lacks expressive power. The latter has expressive power, but the computational cost is too heavy on edge devices. To solve these problems, we propose "DynamicISP," which consists of multiple classical ISP functions and dynamically controls the parameters of each frame according to the recognition result of the previous frame. We show our method successfully controls the parameters of multiple ISP functions and achieves state-of-the-art accuracy with low computational cost in single and multi-category object detection tasks.

翻译：图像信号处理器（ISP）在图像识别任务及捕获图像的感知质量中均扮演着重要角色。通常情况下，专家需要投入大量精力手动调整ISP的众多参数，但这些参数往往并非最优。现有文献中，两类技术被广泛研究：基于机器学习的参数调优技术和基于深度神经网络的ISP技术。前者虽轻量但表达能力不足，后者虽表达能力强但对边缘设备而言计算成本过高。为解决这些问题，我们提出"DynamicISP"——它由多个经典ISP功能构成，能根据前一帧的识别结果动态控制当前帧的各参数。实验表明，我们的方法成功实现了对多个ISP参数的动态控制，在单目标及多目标物体检测任务中以较低计算成本达到了当前最优精度。