Vehicle information recognition is crucial in various practical domains, particularly in criminal investigations. Vehicle Color Recognition (VCR) has garnered significant research interest because color is a visually distinguishable attribute of vehicles and is less affected by partial occlusion and changes in viewpoint. Despite the success of existing methods for this task, the relatively low complexity of the datasets used in the literature has been largely overlooked. This research addresses this gap by compiling a new dataset representing a more challenging VCR scenario. The images - sourced from six license plate recognition datasets - are categorized into eleven colors, and their annotations were validated using official vehicle registration information. We evaluate the performance of four deep learning models on a widely adopted dataset and our proposed dataset to establish a benchmark. The results demonstrate that our dataset poses greater difficulty for the tested models and highlights scenarios that require further exploration in VCR. Remarkably, nighttime scenes account for a significant portion of the errors made by the best-performing model. This research provides a foundation for future studies on VCR, while also offering valuable insights for the field of fine-grained vehicle classification.

翻译：车辆信息识别在多个实际领域中至关重要，尤其在刑事侦查中。车辆颜色识别因其作为车辆视觉可区分属性，且受部分遮挡和视角变化影响较小，已引起广泛研究兴趣。尽管现有方法在此任务上取得了成功，但文献中所用数据集的相对低复杂度在很大程度上被忽视了。本研究通过构建一个代表更具挑战性VCR场景的新数据集来填补这一空白。这些图像来源于六个车牌识别数据集，被划分为十一种颜色，其标注通过官方车辆登记信息进行了验证。我们在一个广泛采用的数据集及我们提出的数据集上评估了四种深度学习模型的性能，以建立基准。结果表明，我们的数据集对测试模型构成了更大难度，并凸显了VCR中需要进一步探索的场景。值得注意的是，夜间场景在最佳性能模型的错误中占据了显著比例。本研究为未来VCR研究奠定了基础，同时也为细粒度车辆分类领域提供了有价值的见解。