DOPNet: Dense Object Prediction Network for Multiclass Object Counting and Localization in Remote Sensing Images

Object counting and localization for remote sensing images are effective means to solve large-scale object analysis problems. Nowadays, most counting methods obtain the number of objects by employing convolutional neural network (CNN) to regress a density map of objects. Even if these leading methods have achieved impressive performances, they simply focus on estimating the number of single-class objects, without providing location information and cannot support multiclass objects. To tackle these problems, a point-based network named Dense Object Prediction Network (DOPNet) is proposed for multiclass object counting and localization for remote sensing images. DOPNet differs from the conventional approach of predicting multiple density maps by incorporating category attributes into the predicted objects, enabling the accurate counting and localization of multiclass objects. Specifically, DOPNet adopts a multiscale architecture (MS) to provide dense predictions of object proposals. A scale adaptive feature enhancement module (SAFEM) is designed to predict scales of objects for the suppression of duplicate proposals. Given only point level annotations for training, a pseudo-box generation algorithm is designed to find the most suitable pseudo-box of each annotated object for the supervision of scale learning. Comprehensive experiments prove that DOPNet can achieve preferable performance on challenging benchmarks of counting while providing object locations. Code and pre-trained models are available at https://github.com/Ceoilmp/DOPNet .