UnitBox: An Advanced Object Detection Network (1608.01471v1)

Published 4 Aug 2016 in cs.CV

Abstract: In present object detection systems, the deep convolutional neural networks (CNNs) are utilized to predict bounding boxes of object candidates, and have gained performance advantages over the traditional region proposal methods. However, existing deep CNN methods assume the object bounds to be four independent variables, which could be regressed by the $\ell_2$ loss separately. Such an oversimplified assumption is contrary to the well-received observation, that those variables are correlated, resulting to less accurate localization. To address the issue, we firstly introduce a novel Intersection over Union ($IoU$) loss function for bounding box prediction, which regresses the four bounds of a predicted box as a whole unit. By taking the advantages of $IoU$ loss and deep fully convolutional networks, the UnitBox is introduced, which performs accurate and efficient localization, shows robust to objects of varied shapes and scales, and converges fast. We apply UnitBox on face detection task and achieve the best performance among all published methods on the FDDB benchmark.

Authors (5)

Jiahui Yu (65 papers)
Yuning Jiang (106 papers)
Zhangyang Wang (375 papers)
Zhimin Cao (10 papers)
Thomas Huang (48 papers)

Citations (1,303)

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Summary

Overview of "UnitBox: An Advanced Object Detection Network"

The paper "UnitBox: An Advanced Object Detection Network" presents a novel and effective approach to object detection using convolutional neural networks (CNNs). In particular, the authors propose a new loss function called Intersection over Union ( $IoU$ ) loss, which aims to enhance the accuracy of bounding box predictions traditionally hindered by the $\ell_2$ loss assumption of independent bounding box variables. The development of the UnitBox network demonstrates the benefits of this new approach, achieving state-of-the-art performance in the FDDB benchmark for face detection.

Key Contributions

$IoU$ Loss Function:
- The paper introduces the $IoU$ loss function, addressing the deficiencies in the commonly used $\ell_2$ loss for bounding box regression. The $\ell_2$ loss treats the four sides of a bounding box as independent variables, whereas the $IoU$ loss considers them as a correlated unit. This correlation improves the precision of object localization and speeds up convergence during training.
UnitBox Network:
- The UnitBox network leverages a fully convolutional network architecture adapted from the VGG-16 model. The network incorporates two branches: one for predicting confidence scores and another for predicting bounding boxes directly on the feature maps, refined through the $IoU$ loss.
- This network setup results in a more accurate and efficient object detection, capable of handling objects of varied shapes and scales. The distinct scale-invariance property of the $IoU$ loss enables UnitBox to perform well without the need for multi-scale image pyramids during testing.

Experimental Results

Comparison with $\ell_2$ Loss:
- The experimental evaluation reveals that the $IoU$ loss significantly outperforms the $\ell_2$ loss in terms of both convergence speed and detection accuracy. The $IoU$ loss model achieves better localization with fewer training iterations and maintains robustness across various object scales.
- Figures in the paper illustrate the empirical benefits where bounding boxes predicted with $IoU$ loss exhibit higher precision than those predicted with $\ell_2$ loss, particularly evident from ROC curve comparisons and scale variation tests.
State-of-the-Art Performance:
- Applied to face detection, the UnitBox network demonstrates superior performance over other contemporary methods. The ROC curves and example detection results on FDDB illustrate the high detection accuracy and reliable localization provided by UnitBox.
- The practical efficiency of the UnitBox also stands out, being able to process images at around 12 frames per second, which makes it suitable for real-time detection applications.

Implications and Future Work

The integration of the $IoU$ loss function into the UnitBox network presents essential implications for object detection systems. By treating the bounding box prediction as a unit, it aligns with the inherent correlation between the box boundaries, enhancing detection precision and resulting in faster convergence.

Practical Implications:

The robust and efficient nature of UnitBox under the $IoU$ loss function makes it highly applicable in real-time detection scenarios, not limited to face detection but applicable to various object detection tasks involving complex scenes with varied object scales.

Theoretical Implications:

The $IoU$ loss can be extended to other forms of localization tasks beyond bounding boxes, suggesting a broader impact on machine learning models focusing on precise spatial prediction problems.

Future Developments:

Further research could explore the integration of the $IoU$ loss function with different network architectures or optimization techniques to push the limits of object detection accuracy and efficiency.
Adapting the $IoU$ loss for three-dimensional bounding box prediction or integrating it with attention mechanisms within detection networks might provide additional performance gains and expanded applicability.

In summary, the paper provides valuable insights and a significant methodological advancement in object detection, presenting a robust, precise, and efficient framework with broad potential applications in AI and computer vision fields.

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