A lot a research is focused on object detection and it has achieved significant advances with deep learning techniques in recent years. Inspite of the existing research, these algorithms are not usually optimal for dealing with sequences or images captured by drone-based platforms, due to various challenges such as view point change, scales, density of object distribution and occlusion. In this paper, we develop a model for detection of objects in drone images using the VisDrone2019 DET dataset. Using the RetinaNet model as our base, we modify the anchor scales to better handle the detection of dense distribution and small size of the objects. We explicitly model the channel interdependencies by using Squeeze-and-Excitation (SE) blocks that adaptively recalibrates channel-wise feature responses. This helps to bring significant improvements in performance at a slight additional computational cost. Using this architecture for object detection, we build a custom DeepSORT network for object detection on the VisDrone2019 MOT dataset by training a custom Deep Association network for the algorithm.
We present a unique approach for learning the pulse evolution in a nonlinear fiber using a deep convolutional neural network (CNN) by solving the nonlinear Schrodinger equation (NLSE). Deep network model compression has become widespread for deploying such models in real-world applications. A knowledge distillation (KD) based framework for compressing a CNN is presented here. The student network, termed here as OptiDistillNet has better generalisation, has faster convergence, is faster and uses less number of trainable parameters. This work represents the first effort, to the best of our knowledge, that successfully applies a KD-based technique for any nonlinear optics application. Our tests show that even by reducing the model size by up to 91.2%, we can still achieve a mean square error (MSE) which is very close to the MSE of 1.04*10−5 achieved by the teacher model. The advantages of the suggested model include the use of a simple architecture, fast optimization, and improved accuracy, opening up applications in optical coherent communication systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.