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Object detection based on RGB and infrared images has emerged as a crucial research area in computer vision, and the synergy of RGB-Infrared ensures the robustness of object-detection algorithms under varying lighting conditions. However, the RGB-IR image pairs captured typically exhibit spatial misalignment due to sensor discrepancies, leading to compromised localization performance. Furthermore, since the inconsistent distribution of deep features from the two modalities, directly fusing multi-modal features will weaken the feature difference between the object and the background, therefore interfering with the RGB-Infrared object-detection performance. To address these issues, we propose an adaptive dual-discrepancy calibration network (ADCNet) for misaligned RGB-Infrared object detection, including spatial discrepancy and domain-discrepancy calibration. Specifically, the spatial discrepancy calibration module conducts an adaptive affine transformation to achieve spatial alignment of features. Then, the domain-discrepancy calibration module separately aligns object and background features from different modalities, making the distribution of the object and background of the fusion feature easier to distinguish, therefore enhancing the effectiveness of RGB-Infrared object detection. Our ADCNet outperforms the baseline by 3.3% and 2.5% in m A P 50 on the FLIR and misaligned M3FD datasets, respectively. Experimental results demonstrate the superiorities of our proposed method over the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Object proposals are used in two-stage detectors, such as R-CNN, to generate detection results, including category predictions and refined bounding-boxes. As a result, classification scores are assigned to refined bounding-boxes rather than object proposals. However, this procedure ignores the discrepancy of data distribution between object proposals and refined bounding-boxes. We consider this discrepancy could limit the detection accuracy. Specifically, the foreground/background imbalance on object proposals and inaccurate information from low-IoU proposals could hinder the category prediction. In this paper, we propose a detector called the Multi-Path Detection Calibration Network (PDC-Net) to address this problem. The key idea behind PDC-Net is calibrating detection results from R-CNN by considering the statistical discrepancy between object proposals and refined bounding-boxes. PDC-Net is built on Faster R-CNN. The core component in PDC-Net is the multi-path detection head, in which the base detector (from Faster R-CNN) generates detection results from object proposals and multiple calibration detectors fix incorrect outputs from the base detector using refined bounding-boxes. Experiments reveal that PDC-Net can boost detection results. Our method could reach 83.1% and 43.3% mAP respectively on PASCAL VOC and MSCOCO benchmarks, which is comparable to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Pixel-level segmentation has been widely used to improve object detection. Most of the existing methods refine detection features by adding the constraint of the segmentation branch or by simply embedding high-level segmentation features into detection features within the local receptive field. However, noisy segmentation features are unavoidable in real-word applications and can easily cause false positives. To address this problem, we propose a novel hierarchical context embedding module to effectively embed segmentation features into detection features. The idea of this module is to capture hierarchical context information that includes local objects or parts and nonlocal context features by learning multiple attention maps, and subsequently utilize interdependencies between features to recalibrate noisy segmentation features. Furthermore, we use this module in the proposed gated encoder-decoder network that adaptively aggregates feature maps of different resolutions based on the gate mechanism so that we can embed multiscale segmentation feature maps into detection features for more accurate detection of objects of all sizes. Experimental results demonstrate the effectiveness of the proposed method on the Pascal VOC 2012Seg dataset, the Pascal VOC dataset and the MS COCO dataset. [ABSTRACT FROM AUTHOR]

Head detection plays an important role in localizing and identifying persons from visual data. Most existing methods treat head detection as a specific form of object detection. Head detection is nontrivial due to the considerable difficulty in building the local and global information under conditions of unconstrained pose and orientation. To address these issues, this paper presents an effective adaptive relational network to capture context information, which is greatly helpful to suppress missed detection. We show that the fundamental contextual properties, such as the global shape priors from different heads and the local adjacent relationship between the head and shoulders, can be systematically quantified by visual operators. Specifically, we propose a two-step search algorithm to quantify the global intergroup conflict with adaptive scale, pose and viewpoint. Meanwhile, a structured feature module is introduced to capture the local relation of intraindividual stability. Finally, the global priors and local relation are integrated seamlessly into a single-stage head detector that is end-to-end trainable. An extensive ablation analysis demonstrates the effectiveness of our approach. We achieve state-of-the-art results on two challenging datasets, i.e., HollywoodHeads and Brainwash. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles are an essential component in the realization of Industry 4.0. With drones helping to improve industrial safety and efficiency in utilities, construction, and communication, there is an urgent need for drone-based intelligent applications. In this paper, we develop a unified framework to simultaneously detect and count vehicles from drone images. We first explore why the state-of-the-art detectors fail in highly dense drone scenes, which provides more appropriate insights. Then, we propose an effective loss to push the anchors toward matching the ground-truth boxes as much as possible, specifically designed for scale-adaptive anchor generation. Inspired by attention mechanisms in the human visual system, we maximize the mutual information between object classes and features by combining bottom-up cues with top-down attention mechanisms specifically designed for feature extraction. Finally, we build a counting layer with a regularized constraint related to the number of vehicles. Extensive experiments demonstrate the effectiveness of our approach. For both tasks, our proposed method achieves state-of-the-art results on all four challenging datasets. In particular, our results reduce error by a larger factor than previous methods. [ABSTRACT FROM AUTHOR]

Object part segmentation is an important and challenging task in computer vision. The existing supervised part segmentation methods need pixel level training data which leads to a huge workload for the user. In this paper a weakly supervised part segmentation method is proposed which segments part regions from multiple images by only several seeds on an image. Two aspects such as seed propagation among multiple images and part generation from seeds are considered. The first aspect is to generate part seeds in each image in terms of seed propagation which is accomplished by part matching combined with latent object regions. We fuse the local part matching and global shape cosegmentation to avoid the noise propagation. The second aspect is to segment part regions from object regions and part seeds which is formulated as the object shape decomposition model. The shape convexity analysis and seed location are fused to accomplish the decomposition and the final part segmentation. The proposed method is verified on the PASCAL 2010 dataset Bird dataset Cat-Dog dataset and UCF Sports Actions dataset. Experimental results demonstrate the effectiveness of the proposed method with larger intersection over union (IOU) values compared with existing weakly supervised part generation methods. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel feature adaptive co-segmentation method that can learn adaptive features of different image groups for accurate common objects segmentation. We also propose image complexity awareness for adaptive feature learning. In the proposed method, the original images are first ranked according to the image complexities that are measured by superpixel changing cue and object detection cue. Then, the unsupervised segments of the simple images are used to learn the adaptive features, which are achieved using an expectation-minimization algorithm combining l 1-regularized least squares optimization with the consideration of the confidence of the simple image segmentation accuracies and the fitness of the learned model. The error rate of the final co-segmentation is tested by the experiments on different image groups and verified to be lower than the existing state-of-the-art co-segmentation methods. [ABSTRACT FROM AUTHOR]

Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, we propose a novel end-to-end Adaptively Aspect Ratio Multi-Scale Network (A 2 RMNet) to solve this problem. On the one hand, we design a multi-scale feature gate fusion network to adaptively integrate the multi-scale features of objects. This network is composed of gate fusion modules, refine blocks and region proposal networks. On the other hand, an aspect ratio attention network is leveraged to preserve the aspect ratios of objects, which alleviates the excessive shape distortions of objects caused by aspect ratio changes during training. Experiments show that the proposed A 2 RMNet significantly outperforms the previous state of the arts on the DOTA dataset, NWPU VHR-10 dataset, RSOD dataset and UCAS-AOD dataset by 5.73 % , 7.06 % , 3.27 % and 2.24 % , respectively. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an automatic human body segmentation system which mainly consists of human body detection and object segmentation. Firstly, an automatic human body detector is designed to provide hard constraints on the object and background for segmentation. And a coarse-to-fine segmentation strategy is employed to deal with the situation of partly detected object. Secondly, background contrast removal (BCR) and self-adaptive initialization level set (SAILS) are proposed to solve the tough segmentation problems of the high contrast at object boundary and/or similar colors existing in the object and background. Finally, an object updating scheme is proposed to detect and segment new object when it appears in the scene. Experimental results demonstrate that our body segmentation system works very well in the live video and standard sequences with complex background. [Copyright &y& Elsevier]

The generation of object proposals plays an important role in object detection. Most existing methods produce object proposals by using bottom-up cues, such as closed contour or superpixel. In this paper, we propose a novel method to improve the ranking of object proposals by combining bottom-up cues with top-down information of objectivity. Firstly, we utilize the bottom-up method to generate initial object proposals of the given test image. Then we retrieve its top-k similar images from training images set. Considering both appearance and spatial similarity between initial object proposals and the ground truth bounding boxes of these top-k similar images, we obtain the top-down guided scores of initial object proposals. Finally, the refined score of each initial object proposal is modeled as a fusion of the bottom-up score and the top-down score. Experiments show that our method achieves better performance compared with the state-of-art on the Pascal VOC2007 dataset. [ABSTRACT FROM AUTHOR]