Speed/accuracy trade-offs for modern convolutional object detectors

The goal of this paper is to serve as a guide for selecting a detection architecture that achieves the right speed/memory/accuracy balance for a given application and platform. To this end, we investigate various ways to trade accuracy for speed and memory usage in modern convolutional object detection systems. A number of successful systems have been proposed in recent years, but apples-to-apples comparisons are difficult due to different base feature extractors (e.g., VGG, Residual Networks), different default image resolutions, as well as different hardware and software platforms. We present a unified implementation of the Faster R-CNN [Ren et al., 2015], R-FCN [Dai et al., 2016] and SSD [Liu et al., 2015] systems, which we view as "meta-architectures" and trace out the speed/accuracy trade-off curve created by using alternative feature extractors and varying other critical parameters such as image size within each of these meta-architectures. On one extreme end of this spectrum where speed and memory are critical, we present a detector that achieves real time speeds and can be deployed on a mobile device. On the opposite end in which accuracy is critical, we present a detector that achieves state-of-the-art performance measured on the COCO detection task.Comment: Accepted to CVPR 201

Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single Shot Multibox Detector

Рассмотрено применение современного метода обнаружения объектов на изображении - Single Shot Multibox Detector. Обучающая и тестовая выборки соответственно содержат 3000 и 7000 изображений, сделанные монокулярной камерой, установленной в транспортном средстве, движущемся по загородным шоссе в светлое время суток. На каждом изображении размечены области расположения автомобиле

Approaches to Improving the Pre-Excavation Detection of Inhumations

As large scale landscape surveys continue to increase in commercial and research archaeogeophysics, there is still a markedly low ability to geophysically detect and interpret archaeological and forensic inhumations in some instances. The aim of this ongoing research project is to improve data acquisition by implementing an interactive ad hoc workflow model for determining appropriate methodologies for ground-penetrating radar (GPR) surveys, to improve data processing speed, and reduce observer error. Can the confidence of manual interpretations of GPR data be improved by adapting machine learning libraries for automatic object extraction and classification to GPR data based on a training dataset comprised of ground-truthed real GPR data and simulated GPR data

Symbol detection in online handwritten graphics using Faster R-CNN

Symbol detection techniques in online handwritten graphics (e.g. diagrams and mathematical expressions) consist of methods specifically designed for a single graphic type. In this work, we evaluate the Faster R-CNN object detection algorithm as a general method for detection of symbols in handwritten graphics. We evaluate different configurations of the Faster R-CNN method, and point out issues relative to the handwritten nature of the data. Considering the online recognition context, we evaluate efficiency and accuracy trade-offs of using Deep Neural Networks of different complexities as feature extractors. We evaluate the method on publicly available flowchart and mathematical expression (CROHME-2016) datasets. Results show that Faster R-CNN can be effectively used on both datasets, enabling the possibility of developing general methods for symbol detection, and furthermore, general graphic understanding methods that could be built on top of the algorithm.Comment: Submitted to DAS-201

Towards lightweight convolutional neural networks for object detection

We propose model with larger spatial size of feature maps and evaluate it on object detection task. With the goal to choose the best feature extraction network for our model we compare several popular lightweight networks. After that we conduct a set of experiments with channels reduction algorithms in order to accelerate execution. Our vehicle detection models are accurate, fast and therefore suit for embedded visual applications. With only 1.5 GFLOPs our best model gives 93.39 AP on validation subset of challenging DETRAC dataset. The smallest of our models is the first to achieve real-time inference speed on CPU with reasonable accuracy drop to 91.43 AP.Comment: Submitted to the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S) in conjunction with the 14th IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2017