This paper describes AutoFocus, an efficient multi-scale inference algorithm for deep-learning based object detectors. Instead of processing an entire image pyramid, AutoFocus adopts a coarse to fine approach and only processes regions which are likely to contain small objects at finer scales. This is achieved by predicting category agnostic segmentation maps for small objects at coarser scales, called FocusPixels. FocusPixels can be predicted with high recall, and in many cases, they only cover a small fraction of the entire image. To make efficient use of FocusPixels, an algorithm is proposed which generates compact rectangular FocusChips which enclose FocusPixels. The detector is only applied inside FocusChips, which reduces computation while processing finer scales. Different types of error can arise when detections from FocusChips of multiple scales are combined, hence techniques to correct them are proposed. AutoFocus obtains an mAP of 47.9 (68.3 at 50 overlap) on the COCO test-dev set while processing 6.4 images per second on a Titan X (Pascal) GPU. This is 2.5X faster than our multi-scale baseline detector and matches its mAP. The number of pixels processed in the pyramid can be reduced by 5X with a 1 drop in mAP. AutoFocus obtains more than 10 mAP gain compared to RetinaNet but runs at the same speed with the same ResNet-101 backbone.
AutoFocus alleviates this problem to a large extent and is designed to provide a smooth trade-off between speed and accuracy. It shows that it is possible to predict the presence of a small object at a coarser scale (referred to as FocusPixels) which enables avoiding computation in large regions of the image at finer scales. These are different from object proposals @cite_20 @cite_31 @cite_7 where region candidates need to have a tight overlap with objects. Learning to predict FocusPixels is an easier task and does not require instance-level reasoning. AutoFocus shares the motivation with saliency based methods which perform a guided search while processing images @cite_33 @cite_5 @cite_58 @cite_54 , but it is designed to predict small objects in coarser scales and they need not be salient.
@cite_33 @cite_7 @cite_54 @cite_5 @cite_31 @cite_58 @cite_20
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This paper describes AutoFocus, an efficient multi-scale inference algorithm for deep-learning based object detectors. Instead of processing an entire image pyramid, AutoFocus adopts a coarse to fine approach and only processes regions which are likely to contain small objects at finer scales. This is achieved by predicting category agnostic segmentation maps for small objects at coarser scales, called FocusPixels. FocusPixels can be predicted with high recall, and in many cases, they only cover a small fraction of the entire image. To make efficient use of FocusPixels, an algorithm is proposed which generates compact rectangular FocusChips which enclose FocusPixels. The detector is only applied inside FocusChips, which reduces computation while processing finer scales. Different types of error can arise when detections from FocusChips of multiple scales are combined, hence techniques to correct them are proposed. AutoFocus obtains an mAP of 47.9 (68.3 at 50 overlap) on the COCO test-dev set while processing 6.4 images per second on a Titan X (Pascal) GPU. This is 2.5X faster than our multi-scale baseline detector and matches its mAP. The number of pixels processed in the pyramid can be reduced by 5X with a 1 drop in mAP. AutoFocus obtains more than 10 mAP gain compared to RetinaNet but runs at the same speed with the same ResNet-101 backbone. Digital cameras are equipped with passive autofocus mechanisms where a lens is focused using only the camera's optical system and an algorithm for controlling the lens. The speed and accuracy of the autofocus algorithm are crucial to user satisfaction. In this paper, we address the problems of identifying the global optimum and significant local optima (or peaks) when focusing an image. We show that supervised machine learning techniques can be used to construct a passive autofocus heuristic for these problems that out-performs an existing hand-crafted heuristic and other baseline methods. In our approach, training and test data were produced using an offline simulation on a suite of 25 benchmarks and correctly labeled in a semi-automated manner. A decision tree learning algorithm was then used to induce an autofocus heuristic from the data. The automatically constructed machine-learning-based (ml-based) heuristic was compared against a previously proposed hand-crafted heuristic for autofocusing and other baseline methods. In our experiments, the ml-based heuristic had improved speed--reducing the number of iterations needed to focus by 37.9 on average in common photography settings and 22.9 on average in a more difficult focus stacking setting--while maintaining accuracy. Recent development of compressive sensing has greatly benefited radar imaging problems. In this paper, we investigate the problem of obtaining enhanced targets such as ships and airplanes, where targets often exhibit structured sparsity. A novel structured sparsity-driven autofocus algorithm is proposed based on sparse Bayesian framework.The structured sparse prior is imposed on the target scene in a statistical manner. Based on a statistical framework, the proposed algorithm can simultaneously cope with structured sparse recovery and phase error correction problem. The focused high-resolution radar image can be obtained by iteratively estimating scattering coefficients and phase. Due to the structured sparse constraint, the proposed algorithm can desirably preserve the target region and alleviate over-shrinkage problem, compared to previous sparsity-driven auto-focus approaches.Moreover, to accelerate convergence rate of the algorithm, we propose to adaptively eliminate noise-only range cells in estimating phase errors. The selection is conveniently conducted based on the parameters controlling sparsity degree of the signal in the proposed hierarchical model.The simulated and real data experimental results demonstrate that the proposed algorithm can obtain more concentrated images with much smaller number of iterations, particularly in low SNR and highly under-sampling scenarios. HighlightsA novel structured sparsity-driven autofocus algorithm is proposed based on sparse Bayesian framework.To accelerate convergence rate of the algorithm, we propose to adaptively eliminate noise-only range cells in phase error estimation stage.The proposed algorithm can obtain more concentrated images with much smaller number of iterations, particularly in low SNR and highly under-sampling scenarios. Autofocus algorithms are used to restore images in nonideal synthetic aperture radar imaging systems. In this paper, we propose a bilinear parametric model for the unknown image and the nuisance phase parameters and derive an efficient maximum-likelihood autofocus (MLA) algorithm. In the special case of a simple image model and a narrow range of look angles, MLA coincides with the successful multichannel autofocus (MCA). MLA can be interpreted as a generalization of MCA to a larger class of models with a larger range of look angles. We analyze its advantages over previous extensions of MCA in terms of identifiability conditions and noise sensitivity. As a byproduct, we also propose numerical approximations to the difficult constant modulus quadratic program that lies at the core of these algorithms. We demonstrate the superior performance of our proposed methods using computer simulations in both the correct and mismatched system models. MLA performs better than other methods, both in terms of the mean squared error and visual quality of the restored image.