HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection (1604.00600v1)

Abstract: Almost all of the current top-performing object detection networks employ region proposals to guide the search for object instances. State-of-the-art region proposal methods usually need several thousand proposals to get high recall, thus hurting the detection efficiency. Although the latest Region Proposal Network method gets promising detection accuracy with several hundred proposals, it still struggles in small-size object detection and precise localization (e.g., large IoU thresholds), mainly due to the coarseness of its feature maps. In this paper, we present a deep hierarchical network, namely HyperNet, for handling region proposal generation and object detection jointly. Our HyperNet is primarily based on an elaborately designed Hyper Feature which aggregates hierarchical feature maps first and then compresses them into a uniform space. The Hyper Features well incorporate deep but highly semantic, intermediate but really complementary, and shallow but naturally high-resolution features of the image, thus enabling us to construct HyperNet by sharing them both in generating proposals and detecting objects via an end-to-end joint training strategy. For the deep VGG16 model, our method achieves completely leading recall and state-of-the-art object detection accuracy on PASCAL VOC 2007 and 2012 using only 100 proposals per image. It runs with a speed of 5 fps (including all steps) on a GPU, thus having the potential for real-time processing.

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

The paper, "HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection," authored by Tao Kong, Anbang Yao, Yurong Chen, and Fuchun Sun, introduces a novel deep hierarchical approach for object detection known as HyperNet. The proposed method integrates region proposal generation and object detection into a single, end-to-end trainable framework using a sophisticated feature aggregation strategy termed Hyper Feature.

Key Contributions and Methodology

The principal innovation of HyperNet is the construction of a Hyper Feature, which aggregates hierarchical feature maps from different convolutional layers, combining deep, semantically rich features with finer, higher-resolution ones. This multi-scale integration enhances both the recall and precision of region proposals and object detection. The Hyper Features are then utilized in a unified network structure to simultaneously generate proposals and conduct object detection. This cohesive framework allows for efficient forward propagation and end-to-end learning.

HyperNet's architecture leverages the VGG16 model for its deep convolutional layers, producing feature maps at multiple resolutions. The aggregated Hyper Feature is processed through a specialized region proposal generation network, which significantly reduces the number of necessary proposals while maintaining high recall rates. The final detection stage refines these proposals and classifies objects using a modified Fast R-CNN framework with enhanced feature integration.

Numerical Results and Empirical Validation

HyperNet demonstrates superior performance across several evaluation metrics. Notably, on the PASCAL VOC 2007 dataset:

• Recall Rates: HyperNet achieves 95% recall with just 50 proposals and 97% recall with 100 proposals, outperforming existing methods like the Region Proposal Network (RPN) which requires significantly more proposals to achieve similar results.
• Detection Accuracy: On the PASCAL VOC 2007 test set, HyperNet achieves a mean Average Precision (mAP) of 76.3%, notably improving upon the 70.0% mAP of Fast R-CNN and the 73.2% mAP of Faster R-CNN.

The improved performance is particularly evident in the detection of small objects, where HyperNet surpasses Faster R-CNN by substantial margins (e.g., a 10.3 point improvement for bottles and a 12.4 point improvement for potted plants).

Implications and Future Directions

The advancements presented in HyperNet have far-reaching implications for real-world object detection systems. The ability to generate region proposals with high recall using fewer candidate boxes translates to faster processing times and improved efficiency, making HyperNet a viable solution for real-time applications. The proposed system's architecture inherently enables better localization of smaller objects, addressing a significant challenge faced by previous models such as RPN-based approaches.

From a theoretical perspective, HyperNet's architecture suggests that combining multi-scale features from various depths of a convolutional network allows for more comprehensive and enriched feature representations, which are crucial for both proposal generation and object categorization tasks. This concept can be extended to other domains where multi-scale feature aggregation could enhance performance, such as semantic segmentation and instance segmentation tasks.

Conclusion

"HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection" represents a significant step forward in the field of computer vision and deep learning-based object detection. By effectively aggregating hierarchical features and unifying the region proposal and detection processes, HyperNet achieves state-of-the-art accuracy and efficiency. The implications of this work pave the way for further research into multi-scale feature integration and its applications across various computer vision challenges.