Foreword: From "Test Equipment" to "Measurement System" — A Paradigm Shift In the previous article (Article 1: Component Selection), the discussion centered on how to make differentiated selections of sensors, lenses, and SoCs based on the extreme application scenarios of fleet safety cameras. However, specifications on paper and design trade-offs in theory must be validated through rigorous physical testing. Many engineers' understanding of an Image Quality Laboratory (IQ Lab) tends to stop at "a darkroom filled with integrating spheres, light boxes, test charts, and optical benches." Yet for a world-class fleet safety camera development team, the value of an IQ Lab extends far beyond the accumulation of hardware equipment. The essence of an IQ Lab is a high-precision Measurement System. Its existence is to answer the following core questions: How can we ensure that the resolution measured today is consistent with the results measured next month? How can we prove that the lab's test data accurately reflects the performance of edge AI algorithms in the real world? To answer these questions, the mindset must shift from simply "purchasing equipment" to "establishing measurement standards." This means the lab must possess extremely high test Repeatability, Reproducibility, and measurement Traceability. A poorly designed lab introduces hidden environmental errors, leading to misjudgments of component true performance and ultimately incorrect selection decisions. This article provides an in-depth practical guide on how to build an IQ Lab from scratch — one equipped with rigorous measurement strategies, a capability matrix, and Golden Sample management mechanisms — ensuring that every data point produced by the lab becomes an unshakeable decision-making foundation throughout the product lifecycle. Measurement Strategy: Define the Target Before Selecting the Equipment The first step in building an IQ Lab is never to open an equipment supplier's catalog. It is to formulate a clear measurement strategy.