###### Abstract
In [10], the original fisheye image is used without undistortion to avoid the bottleneck in achieving real-time recognition performance. The road object such as vehicle and pedestrian are rotated in the boundary of the original fisheye image, thus the authors proposes a rotation invariant deep neural network. In contrast, a rotation sensitive neural network is proposed in [11] to detect objects in the original fisheye image. The bounding box is rotated to fit the orientation of the detecting object. However, these two investigations detect road or indoor objects which size is normal compared with the image size. In their test images, the sizes of the bounding boxes of pedestrian, vehicle, and computer monitor are generally large compared with the image size. In [12], the original fisheye image is trained without any lens parameters or calibration patterns. Instead, the authors propose a contour-based object detector to cope with the distortion of the fisheye image. A 'distortion shape matching' strategy is proposed to train the contour information of objects using a fisheye image detection network. A small object detection method in the fisheye image is proposed in [13]. The authors propose a concatenated feature pyramid, which is a variant of the Feature Pyramid Network (FPN), to find very small road objects in a fisheye image. They add an additional concatenation network to the original FPN in YOLOv3 to increase the small object detection performance. The second approach is based on the rectification or undistortion of the original fisheye image. The generative-adversarial network is adapted to rectify the fisheye image in [14; 15]. However, these studies require complex computations, hindering the real-time performance that are required from one-stage object detectors. The cylindrical projection model is also used to rectify the fisheye image and to find the 3D objects in the road scene [16]. The authors propose that training the rectilinear images is better than using the original fisheye image for detecting the 3D road objects. The authors use their own fisheye image database for performance comparison. The images are captured by using a side view camera, thus the number of road objects is smaller than that in the standard benchmark. As described above, recent investigations on the fisheye image object detection mostly utilize the original image rather than using rectification or undistortion method. This is due to the strong radial distortion of the fisheye image. Some investigations utilize the conventional cylindrical or spherical rectification; however, the rectified images still contain object size variation due to barrel or pincushion distortions. Therefore, in this paper, we first propose a new spherical-based projection in real-time speed to solve radial distortion and detect small objects with increased pixel information. Second, we propose a multi-level feature concatenation to a convolutional neural network, suggesting three types of concatenated YOLOv3 with Spatial Pyramid Pooling (SPP) module [17; 18; 19; 20]. We evaluate our solution with several public datasets, as well as our new collection of images gathered with a 185\({}^{\circ}\) fisheye lens. The major contributions of this study are noted as follows. * Introduction of a new front-view fisheye dataset consisting of 5K bounding box annotations. * Proposal of an effective spherical projection model on fisheye images based on the size of objects in the dataset. * Proposal and analysis of three feature concatenation methods to reduce small objects detection issues in real-time object detector. Figure 1 shows some examples of object detection bounding boxes from several types of fisheye images. The size of bounding boxes is different according to the size of the detected objects. In the previous investigations, the detected objects are generally large in the image. Thus, the detection is relatively easier than small or tiny objects.