## 1 Introduction  
The use of 360\({}^{\circ}\) omnidirectional images has become more and more popular because they can capture more scene information than traditional images and can meet the increasing demand for broad vision both in industry and daily life. Briefly, 360\({}^{\circ}\) omnidirectional images have been widely used in various fields, such as automatic driving [1], 3D scene reconstruction [2], virtual reality [3], and virtual navigation [4]. The most commonly used method to represent 360\({}^{\circ}\) omnidirectional images is equirectangular projection (ERP), but the projected images can lose continuity at the left and right edges and produce severe distortion. As shown in Figure 1, the left and right edges of the image are discontinuous and the upper and lower parts are severely distorted. Furthermore, convolutional neural networks (CNN) have been widely used in various fields of computer vision and have achieved excellent performance. For instance, [PERSON] et al. [5] proposed a new method to diagnose cancer using CNN. [PERSON] et al. [6] used CNN to intelligently identify weed density in soybean crop fields. [PERSON] et al. [7]estimated traffic density based on CNN to facilitate traffic management. [PERSON] et al. [8] applied the LeNet-5 to the task of recognizing handwritten fonts and achieved remarkable results. [PERSON] et al. [9] applied the AlexNet to achieve strong performance on the ImageNet dataset. With in-depth study of convolutional neural networks, more and more advanced network models have been proposed, including NiN [10], VGG [11], GoogleNet [12], ResNet [13], and other excellent network models [14; 15; 16; 17; 18]. At present, most follow-up research being conducted uses these advanced networks as the backbone network to extract better features. Currently, there are many convolutional neural networks obtained using traditional images after training. Since these models do not deal with distortion and discontinuity, the performance obtained by these networks when processing 360\({}^{\circ}\) omnidirectional images is not satisfactory. For 360\({}^{\circ}\) omnidirectional images, some existing models [19; 20; 21] cannot be combined with the pre-trained weights of advanced models since these models are built based on new convolution, which causes difficulty in training of the model. Moreover, many methods [22; 23; 24; 25; 26; 27] use reprojection approaches when dealing with discontinuity and distortion, which result in huge computational overhead that can affect the training and inference speed of the model. To solve these problems, new methods are proposed here. In general, the contributions of the paper can be summarized as follows:  
* A novel edge continuity distortion-aware block (ECDAB) and a convolutional row-column attention block (CRCAB) are proposed. ECDAB extracts continuous features through recombination features and group convolution is performed through segmentation features using different convolution kernels between different blocks to alleviate distortion. CRCAB enables interaction of spatial information in a unique way to enhance the receptive field and capacity for feature expression. * To reduce the memory overhead of CRCAB for very high and wide images or features, we propose an improved convolutional row-column attention block (ICRCAB). * Based on experimental investigation of classification and object detection using 360\({}^{\circ}\) omnidirectional images, it is demonstrated that better performance than for the baseline model is obtained by the network using ECDAB or CRCAB. Moreover, it is shown that CRCAB also performs well when using traditional images. The rest of the paper is organized as follows: Section 2 details related studies that consider 360\({}^{\circ}\) omnidirectional images. Section 3 describes the details of the proposed ECDAB, CRCAB and ICRCAB. Section 4 describes the classification and object detection experiments undertaken. Finally, Section 5 concludes the paper and discusses potential future work.