In CNN-based object detection methods, region proposal becomes a bottleneck when objects exhibit significant scale variation, occlusion or truncation. In addition, these methods mainly focus on 2D object detection and cannot estimate detailed properties of objects. In this paper, we propose subcategory-aware CNNs for object detection. We introduce a novel region proposal network that uses subcategory information to guide the proposal generating process, and a new detection network for joint detection and subcategory classification. By using subcategories related to object pose, we achieve state-of-the-art performance on both detection and pose estimation on commonly used benchmarks.
We can categorize the state-of-the-art CNN-based object detection methods into two classes: one-stage detection and two-stage detection. In one-stage detection, such as the Overfeat @cite_25 framework, a CNN directly processes an input image, and outputs object detections. In two-stage detection, such as R-CNNs @cite_27 @cite_31 @cite_6 , region proposals are first generated from an input image, where different region proposal methods can be employed @cite_21 @cite_23 @cite_7 . Then these region proposals are fed into a CNN for classification and location refinement. It is debatable which detection paradigm is better. We adopt the two-stage detection framework in this work, and consider the region proposal process to be the coarse detection step in coarse-to-fine detection @cite_26 . We propose a novel RPN motivated by @cite_6 and demonstrate its advantages.
@cite_26 @cite_7 @cite_21 @cite_6 @cite_27 @cite_23 @cite_31 @cite_25
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Many modern approaches for object detection are two-staged pipelines. The first stage identifies regions of interest which are then classified in the second stage. Faster R-CNN is such an approach for object detection which combines both stages into a single pipeline. In this paper we apply Faster R-CNN to the task of company logo detection. Motivated by its weak performance on small object instances, we examine in detail both the proposal and the classification stage with respect to a wide range of object sizes. We investigate the influence of feature map resolution on the performance of those stages. Based on theoretical considerations, we introduce an improved scheme for generating anchor proposals and propose a modification to Faster R-CNN which leverages higher-resolution feature maps for small objects. We evaluate our approach on the FlickrLogos dataset improving the RPN performance from 0.52 to 0.71 (MABO) and the detection performance from 0.52 to @math (mAP). Object proposals have recently emerged as an essential cornerstone for object detection. The current state-of-the-art object detectors employ object proposals to detect objects within a modest set of candidate bounding box proposals instead of exhaustively searching across an image using the sliding window approach. However, achieving high recall and good localization with few proposals is still a challenging problem. The challenge becomes even more difficult in the context of autonomous driving, in which small objects, occlusion, shadows, and reflections usually occur. In this paper, we present a robust object proposals re-ranking algorithm that effectivity re-ranks candidates generated from a customized class-independent 3DOP (3D Object Proposals) method using a two-stream convolutional neural network (CNN). The goal is to ensure that those proposals that accurately cover the desired objects are amongst the few top-ranked candidates. The proposed algorithm, which we call DeepStereoOP, exploits not only RGB images as in the conventional CNN architecture, but also depth features including disparity map and distance to the ground. Experiments show that the proposed algorithm outperforms all existing object proposal algorithms on the challenging KITTI benchmark in terms of both recall and localization. Furthermore, the combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results of all three KITTI object classes. HighlightsWe present a robust object proposals re-ranking algorithm for object detection in autonomous driving.Both RGB images and depth features are included in the proposed two-stream CNN architecture called DeepStereoOP.Initial object proposals are generated from a customized class-independent 3DOP method.Experiments show that the proposed algorithm outperforms all existing object proposals algorithms.The combination of DeepStereoOP and Fast R-CNN achieves one of the best detection results on KITTI benchmark. Recent CNN based object detectors, no matter one-stage methods like YOLO, SSD, and RetinaNe or two-stage detectors like Faster R-CNN, R-FCN and FPN are usually trying to directly finetune from ImageNet pre-trained models designed for image classification. There has been little work discussing on the backbone feature extractor specifically designed for the object detection. More importantly, there are several differences between the tasks of image classification and object detection. 1. Recent object detectors like FPN and RetinaNet usually involve extra stages against the task of image classification to handle the objects with various scales. 2. Object detection not only needs to recognize the category of the object instances but also spatially locate the position. Large downsampling factor brings large valid receptive field, which is good for image classification but compromises the object location ability. Due to the gap between the image classification and object detection, we propose DetNet in this paper, which is a novel backbone network specifically designed for object detection. Moreover, DetNet includes the extra stages against traditional backbone network for image classification, while maintains high spatial resolution in deeper layers. Without any bells and whistles, state-of-the-art results have been obtained for both object detection and instance segmentation on the MSCOCO benchmark based on our DetNet (4.8G FLOPs) backbone. The code will be released for the reproduction. Object detection is a fundamental problem in image understanding. One popular solution is the R-CNN framework [15] and its fast versions [14, 27]. They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories. Despite that we are handling with two relatively easier tasks, they are not solved perfectly and there's still room for improvement. In this paper, we push the "divide and conquer" solution even further by dividing each task into two sub-tasks. We call the proposed method "CRAFT" (Cascade Regionproposal-network And FasT-rcnn), which tackles each task with a carefully designed network cascade. We show that the cascade structure helps in both tasks: in proposal generation, it provides more compact and better localized object proposals, in object classification, it reduces false positives (mainly between ambiguous categories) by capturing both inter-and intra-category variances. CRAFT achieves consistent and considerable improvement over the state-of the-art on object detection benchmarks like PASCAL VOC 07 12 and ILSVRC.