A Simulation Test Platform for Visual Recognition Systems and Its Application

Visual Recognition systems are widely used in fields such as autonomous driving and intelligent security. However, testing these systems faces challenges including restricted scenarios, limited evaluation dimensions, and poor cross-platform compatibility. This paper proposes a universal simulation test platform for Visual Recognition systems, constructing a standardized testing framework through dynamic and static scene simulation, scalable data generation, multi-dimensional monitoring, and hardware-compatible architecture. Specific innovations include: 1) Dynamic and static scene simulation technology, which invokes image generation models (such as foreground-background fusion models) through extensible model management interfaces and uses path clipping technology to generate diversified test data; 2) A multi-dimensional monitoring system that integrates dynamic performance metrics (such as latency jitter and memory peak values) with traditional monitoring indicators; 3) Modular hardware-compatible design, integrating a simulation operation board to support model performance analysis on heterogeneous platforms such as GPUs and FPGAs. Based on this platform, application verification was conducted using several typical models and datasets. Experiments show that the platform can effectively complete multi-dimensional monitoring and testing of Visual Recognition systems under different scenarios and hardware architectures, providing strong data support and technical backing for system performance and quality evaluation, as well as further improvements.

Ҳали таржима қилинмаган