Japanese comics (manga) are popular content worldwide. In order to acquire metadata from manga images, techniques automatic recognition of manga content have been studied. Recently, Convolutional Neural Network (CNN) has been applied to object detection in manga images. R-CNN and Fast R-CNN generate region proposals by Selective Search. Faster R-CNN generates them using CNN layers called Region Proposal Network (RPN). Single Shot MultiBox Detector (SSD), the latest detection method, performs object classification and box adjustment for small regions in an image. These methods are effective to natural images. However, it is unclear whether such methods work properly to manga images or not, since those image features are different from natural images. In this paper, we examine the effectiveness of manga object detection by comparing Fast R-CNN, Faster R-CNN, and SSD. Here, manga objects are panel layout, speech balloon, character face, and text. Experimental results show that Fast R-CNN is effective for panel layout and speech balloon, whereas Faster R-CNN is effective for character face and text.