Object detection is a fundamental problem in image understanding. One popular solution is the R-CNN framework and its fast versions. 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 Region-proposal-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.
Many works are proposed to get more compact proposals, which can be divided into two types: the unsupervised grouping style and the supervised classification style. The most popular method in grouping style is the Selective Search @cite_17 , which hierarchically groups super-pixels generated through @cite_24 to form general object proposals. Other typical grouping style proposal methods include the EdgeBox @cite_21 which is faster and MCG @cite_9 which is more compact. With around 2000 proposals kept for each image, a recall rate of $98 In the supervised camp, the proposal generation problem is defined as a classification and or regression problem. Typical methods include the BING @cite_18 and Multi-box @cite_20 @cite_28 . The BING uses the binary feature and SVM to efficiently classify objects from background. The Multi-box uses CNN to regress the object location in an end-to-end manner. A recently proposed promising solution is the Region Proposal Network (RPN) @cite_4 , where a multi-task fully convolutional network is used to jointly estimate proposal location and assign each proposal with a confidence score. The number of proposals is also reduced to be less than 300 with higher recall rate. We use the RPN as the baseline proposal algorithm in CRAFT.
@cite_18 @cite_4 @cite_28 @cite_9 @cite_21 @cite_24 @cite_20 @cite_17
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We are motivated by the need for an object proposal generation algorithm that achieves a good balance between proposal localization quality, object recall and computational efficiency. We propose a novel object proposal algorithm BING++ which inherits the good computational efficiency of BING BingObj2014 but significantly improves its proposal localization quality. Central to our success is based on the observation that good bounding boxes are those that tightly cover objects. Edge features, which can be computed efficiently, play a critical role in this context. We propose a new algorithm that recursively improves BING's proposals by exploiting the fact that edges in images are typically associated with object boundaries. BING++ improves proposals recursively by incorporating nearest edge points (to proposal boundary pixels) to obtain a tighter bounding box. This operation has linear computational complexity in number of pixels and can be done efficiently using distance transform. Superpixel merging techniques are then employed as post-processing to further improve the proposal quality. Empirically on the VOC2007 dataset, using @math proposals and IoU threshold 0.5, our method achieves 95.3 object detection recall (DR), 79.2 mean average best overlap (MABO), and 68.7 mean average precision (mAP) on object detection over 20 object classes within an average time of 0.009 seconds per image. Current high-quality object detection approaches use the scheme of salience-based object proposal methods followed by post-classification using deep convolutional features. This spurred recent research in improving object proposal methods. However, domain agnostic proposal generation has the principal drawback that the proposals come unranked or with very weak ranking, making it hard to trade-off quality for running time. This raises the more fundamental question of whether high-quality proposal generation requires careful engineering or can be derived just from data alone. We demonstrate that learning-based proposal methods can effectively match the performance of hand-engineered methods while allowing for very efficient runtime-quality trade-offs. Using the multi-scale convolutional MultiBox (MSC-MultiBox) approach, we substantially advance the state-of-the-art on the ILSVRC 2014 detection challenge data set, with @math mAP for a single model and @math mAP for an ensemble of two models. MSC-Multibox significantly improves the proposal quality over its predecessor MultiBox method: AP increases from @math to @math for the ILSVRC detection challenge. Finally, we demonstrate improved bounding-box recall compared to Multiscale Combinatorial Grouping with less proposals on the Microsoft-COCO data set. We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object proposals by exploring efficiently their combinatorial space. We also present Single-scale Combinatorial Grouping (SCG), a faster version of MCG that produces competitive proposals in under five seconds per image. We conduct an extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD, and COCO datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object proposals. Existing object proposal approaches use primarily bottom-up cues to rank proposals, while we believe that objectness is in fact a high level construct. We argue for a data-driven, semantic approach for ranking object proposals. Our framework, which we call DeepBox, uses convolutional neural networks (CNNs) to rerank proposals from a bottom-up method. We use a novel four-layer CNN architecture that is as good as much larger networks on the task of evaluating objectness while being much faster. We show that DeepBox significantly improves over the bottom-up ranking, achieving the same recall with 500 proposals as achieved by bottom-up methods with 2000. This improvement generalizes to categories the CNN has never seen before and leads to a 4.5-point gain in detection mAP. Our implementation achieves this performance while running at 260 ms per image.