Perceptual Generative Adversarial Networks for Small Object Detection (1706.05274v2)

Abstract: Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.

• The paper introduces a novel GAN structure that super-resolves small object features through residual learning and perceptual loss.
• It employs an adversarial training strategy that iteratively refines both generator and discriminator for enhanced detection performance.
• Empirical results on benchmarks like Tsinghua-Tencent 100K and Caltech show significant improvements in recall and accuracy for small objects such as traffic signs and pedestrians.

Perceptual Generative Adversarial Networks for Small Object Detection

This paper introduces a novel approach to small object detection by leveraging a specialized Generative Adversarial Network (GAN), termed the Perceptual GAN. The proposed model addresses the inherent challenges of detecting small objects, which typically suffer from low-resolution representation and inadequate discriminative features. Traditional methods that attempt to improve detection via multi-scale representation often fall short due to increased computational costs and limited performance enhancements. In contrast, this research presents a unified architecture that effectively enhances the internal representations of small objects, transforming them into "super-resolved" representations akin to those of larger objects, subsequently improving detection accuracy.

Key Contributions

1. Perceptual GAN Structure: The Perceptual GAN consists of a generator and a discriminator. The generator is responsible for transforming poor representations of small objects into super-resolved versions. It utilizes residual learning to inject detailed low-level features, enhancing the small object representations. Concurrently, the discriminator distinguishes between these generated representations and authentic large object representations, incorporating a perceptual loss to ensure the improvements benefit detection tasks.
2. Training Dynamics: The framework employs an adversarial training strategy, optimizing the generator and discriminator iteratively. This process enhances the discriminator's ability to distinguish between real and generated features, while guiding the generator in producing representations that closely mimic large-object features for superior detection performance.
3. Empirical Validation: Evaluation on the Tsinghua-Tencent 100K and Caltech benchmarks indicates that Perceptual GAN significantly outperforms contemporary methods in detecting small objects, including traffic signs and pedestrians. The numerical results demonstrate remarkable improvements in recall and accuracy, particularly for small object subsets.
4. Theoretical and Practical Implications: By emphasizing intrinsic structural correlations between objects of varying scales, this approach not only addresses small object detection but also lays groundwork for further refinement of detection frameworks. The research highlights the importance of understanding and leveraging detailed features for enhanced performance across different object scales.

Results and Implications

The results presented in the paper exhibit a notable increase in detection accuracy and recall. Specifically, the method achieves significant gains in detecting small traffic signs across various categories, as well as improved performance in pedestrian detection tasks. The Perceptual GAN's ability to create meaningful representations from scarce data underscores its potential applicability to other fields requiring fine-scale object discrimination.

The paper's findings suggest that future exploration in AI and computer vision could benefit from adopting similar adversarial models for tasks involving detailed feature reconstruction and enhancement. This research offers a promising path for improving detection systems within autonomous driving and intelligent surveillance, where small object identification is crucial.

In conclusion, the Perceptual GAN represents an insightful advancement in small object detection, providing a robust alternative to existing methods with compelling improvements in performance metrics. Its application of adversarial training combined with residual learning opens new avenues for addressing challenges in high-dimensional feature generation.