Visnyk of the Lviv University. Series Applied Mathematics and Computer Science
|Name of the article
||A dropout technique study for the Faster R-CNN detectors with pretrained convolutional neural networks for detecting very simple objects that can be masked
||One of the best object detection methods, the Faster R-CNN, uses a pretrained convolutional neural network allowing to train the detector on small training sets typical in the object detection practice. Convolutional networks are prevented from overfitting by inserting DropOut layers. An open question is whether the DropOut technique improves much the object Faster R-CNN detector accuracy. Therefore, the goal is to show how the DropOut technique influences on the object detector performance. An original image classification dataset for pretraining a convolutional neural network is CIFAR-10. An appropriate convolutional network architecture for classifying CIFAR-10 images has a 50 % DropOut layer inserted in-between two fully-connected layers. Object detection tasks used for training and testing the Faster R-CNN detector are of monochrome images wherein small black rectangles are to be detected. Despite such objects are very simple, they can be masked around some dark localities so that detection would not be easy. One detection task is to detect the black rectangles in suburb house frontal views. Another one is to detect the rectangles in office room views. The suburb view dataset is divided into a training set of 120 images and a testing set of 121 images, every entry with a black rectangle. The office view dataset is divided likewise into a training set of 115 images and a testing set of 100 images, every entry with a black rectangle. Performance of the detector is studied against three training parameters: bounding box overlap ratio for positive training samples, minimum anchor box size, and anchor box pyramid scale factor. The performance is meant by the number of detected objects along with the intersection-over-union. However, neither graphs for the summed intersection-over-unions, nor graphs for the number of detected objects show that the DropOut technique influences on the Faster R-CNN object detector performance. Even for letting miss a few objects and decreasing an accuracy threshold, this influence is not significant. Therefore, a pretrained convolutional neural network to be included into the Faster R-CNN object detector should not contain a DropOut layer, especially if the network is trained much longer with the DropOut layer.