M2-VISD:A Visual Intelligence Evaluation Dataset Based on Virtual Scene*

Most of the existing visual intelligence evaluation datasets like ImageNet or ActivityNet share a common property, namely consisting of many images or videos captured from the real world. This property makes this evaluation datasets suitable for actual applications, but has limitations in achieving the diversity and interactivity of evaluation environments. The currently available datasets do not systematically provide data of different scales and different angles. In order to solve the above problems, this paper constructs a multi-angle and multi-scale data set based on UE4 platform, which can evaluate the performance of the algorithm from both scale and angle in an all-round way. Our experiments show that the algorithms detection performance varies greatly under different scale and different angle. Particularly, in scale-data, when the distance between the camera and the object is less than 50cm or greater than 3200cm, algorithm performance is poor, and in angle-data, the camera’s pitch angle is also poor when it is 18 degree and 0 degree. This result has a guiding role for the correct evaluation of the algorithm performance and provides help for the innovation and optimization of the algorithm.