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The objection detection of panoramic image is the key part of street view, intelligent transportation, automatic driving and other technologies. Due to the shortcomings of existing algorithms in detecting panoramic images, firstly a high-resolution panoramic image dataset is introduced, then the multi-scale feature pyramid networks (MS-RPN) structure is proposed and a new network with Sim-Inception module is designed. The network can extract different scales of objects from different feature layers, so that the small object in the image can also be accurately detected. Finally, the entire detection network is trained by using the dataset constructed in this study. Meanwhile, the ROIPool is replaced by ROIAlign and the loss function is adjusted according to the network structure. The experimental results show that the detection performance on the panoramic dataset is significantly improved by authors' proposed algorithm, which is better than other deep learning algorithms, especially for small object in the image.

Panoramic images have a wide range of applications in many fields with their ability to perceive all-round information. Object detection based on panoramic images has certain advantages in terms of environment perception due to the characteristics of panoramic images, e.g., lager perspective. In recent years, deep learning methods have achieved remarkable results in image classification and object detection. Their performance depends on the large amount of training data. Therefore, a good training dataset is a prerequisite for the methods to achieve better recognition results. Then, we construct a benchmark named Pano-RSOD for panoramic road scene object detection. Pano-RSOD contains vehicles, pedestrians, traffic signs and guiding arrows. The objects of Pano-RSOD are labelled by bounding boxes in the images. Different from traditional object detection datasets, Pano-RSOD contains more objects in a panoramic image, and the high-resolution images have 360-degree environmental perception, more annotations, more small objects and diverse road scenes. The state-of-the-art deep learning algorithms are trained on Pano-RSOD for object detection, which demonstrates that Pano-RSOD is a useful benchmark, and it provides a better panoramic image training dataset for object detection tasks, especially for small and deformed objects.

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