Object detection is an important branch of panoramic image scene understanding. Panoramic images possess characteristics such as a wide field of view, significant distortion, and rich content, which leads to constant changes in the convolutional domain of the panoramic image, thus resulting in the fact that using a convolutional kernel of the same shape is not sufficient to perform convolutional feature extraction on the panoramic image. Therefore, traditional perspective-based object detection algorithms frequently exhibit poor performance on panoramic challenges. An enhanced YOLOX-based panoramic image object detection method is suggested as a solution to this problem. To improve the feature extraction capabilities for distorted objects in panoramic photos, an effective feature extraction network is built by integrating deformable convolution v2 and atrous spatial pyramid pooling (ASPP) into the backbone feature extraction network. Furthermore, the accuracy of panoramic image detection is greatly increased by further strengthening the extraction of image channel features by integrating an enhanced attention mechanism between the feature extraction network and the backbone network. Experimental results demonstrate that the proposed panoramic object detection model achieves an average precision (mAP) of 73.35% on a self-built panoramic image dataset, compared with the existing traditional target detection model, achieving significant performance improvement.