The rise of surveillance systems has led to exponential growth in collected data, enabling several advances in Deep Learning to exploit them and automate tasks for autonomous systems. Vehicle detection is a crucial task in the fields of Intelligent Vehicle Systems and Intelligent Transport systems,making it possible to control traffic density or detect accidents and potential risks. This paper presents an optimal meta-method that can be applied to any instant segmentation model, such as Mask R-CNN or YOLACT++. Using the initial detections obtained by these models and super-resolution, an optimized re-inference is performed, allowing the detection of elements not identified a priori and improving the quality of the rest of the detections. The direct application of super-resolution is limited because instance segmentation models process images according to a fixed dimension. Therefore, in cases where the super-resolved images exceed this fixed size, the model will rescale them again, thus losing the desired effect. The advantages of this meta-method lie mainly in the fact that it is not required to modify the model architecture or re-train it. Regardless of the size of the images given as input, super-resolved areas that fit the defined dimension of the object segmentation model will be generated. After applying our proposal, experiments show an improvement of up to 8.1% for the YOLACT++ model used in the Jena sequence of the CityScapes dataset.

中文翻译：

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通过超分辨率和最大团生成优化实例分割

监控系统的兴起导致收集的数据呈指数级增长，从而使深度学习的多项进步能够利用它们并为自治系统自动执行任务。车辆检测是智能车辆系统和智能交通系统领域的一项重要任务，可以控制交通密度或检测事故和潜在风险。本文提出了一种最优元方法，可应用于任何即时分割模型，例如 Mask R-CNN 或 YOLACT++。使用这些模型和超分辨率获得的初始检测，执行优化的重新推理，允许检测未先验识别的元素并提高其余检测的质量。超分辨率的直接应用受到限制，因为实例分割模型根据固定维度处理图像。因此，如果超分辨率图像超过这个固定大小，模型将再次重新缩放它们，从而失去预期的效果。这种元方法的优点主要在于不需要修改模型架构或重新训练它。无论作为输入的图像大小如何，都将生成符合对象分割模型定义维度的超分辨区域。应用我们的建议后，实验表明在 CityScapes 数据集的 Jena 序列中使用的 YOLACT++ 模型提高了高达 8.1%。从而失去预期的效果。这种元方法的优点主要在于不需要修改模型架构或重新训练它。无论作为输入的图像大小如何，都将生成符合对象分割模型定义维度的超分辨区域。应用我们的建议后，实验表明在 CityScapes 数据集的 Jena 序列中使用的 YOLACT++ 模型提高了高达 8.1%。从而失去预期的效果。这种元方法的优点主要在于不需要修改模型架构或重新训练它。无论作为输入的图像大小如何，都将生成符合对象分割模型定义维度的超分辨区域。应用我们的建议后，实验表明在 CityScapes 数据集的 Jena 序列中使用的 YOLACT++ 模型提高了高达 8.1%。