Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection (2203.16220v1)

Abstract: This study addresses the issue of fusing infrared and visible images that appear differently for object detection. Aiming at generating an image of high visual quality, previous approaches discover commons underlying the two modalities and fuse upon the common space either by iterative optimization or deep networks. These approaches neglect that modality differences implying the complementary information are extremely important for both fusion and subsequent detection task. This paper proposes a bilevel optimization formulation for the joint problem of fusion and detection, and then unrolls to a target-aware Dual Adversarial Learning (TarDAL) network for fusion and a commonly used detection network. The fusion network with one generator and dual discriminators seeks commons while learning from differences, which preserves structural information of targets from the infrared and textural details from the visible. Furthermore, we build a synchronized imaging system with calibrated infrared and optical sensors, and collect currently the most comprehensive benchmark covering a wide range of scenarios. Extensive experiments on several public datasets and our benchmark demonstrate that our method outputs not only visually appealing fusion but also higher detection mAP than the state-of-the-art approaches.

Analysis of "Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection"

The paper by Liu et al. offers a detailed paper on the fusion of infrared and visible images aimed at enhancing object detection capabilities. The traditional approaches in image fusion predominantly focus on improving visual quality, often neglecting the modality differences that can play a crucial role in advanced tasks like object detection. This paper proposes a comprehensive method involving a bilevel optimization approach that jointly addresses image fusion and object detection tasks, leading to significant advancements in the efficacy of such systems.

Methodology

The core methodological advancement introduced by this paper is the Target-aware Dual Adversarial Learning (TarDAL) network. This network uniquely incorporates a generator and dual discriminators to leverage both common and complementary information between infrared and visible modalities. The generator is responsible for generating a fused image that maintains structural information from the infrared input and textural details from the visible input. The discriminators work collaboratively; one targets the foreground features especially visible in infrared, and the other focuses on the background detail typically captured by visible light imaging.

The choice of a cooperative training strategy based on bilevel optimization is notable. This strategy is designed to ensure optimal parameter learning through joint consideration of both tasks, which is the image fusion for improved quality and object detection for accuracy. The methodology is built on leveraging the distinct characteristics of each imaging modality, potentially allowing the system to perform better than systems limited to single-mode or traditional fusion techniques.

Experimental Evaluation

The authors conduct extensive experiments using several datasets, including a new multi-scenario multi-modality benchmark (M³FD) introduced in this paper. M³FD is a valuable contribution, offering high-resolution, synchronized infrared and visible image pairs across diverse environments. It provides a comprehensive testbed for evaluating the proposed method’s effectiveness under various conditions.

Quantitative evaluations demonstrate that TarDAL achieves superior performance in both fusion quality and object detection accuracy compared to existing methods. The paper reports higher detection mean average precision (mAP) figures and notable computational efficiency improvements, marked by fewer parameters and faster inference times. Such results substantiate the dual objective of the system, achieving both computational efficiency and model accuracy.

Theoretical and Practical Implications

The dual adversarial approach offers a framework that enriches the fusion network architecture by incorporating sophisticated mechanisms to harness modality-specific strengths. This approach suggests a pathway toward more intelligent imaging systems in surveillance and autonomous driving applications, where reliability under varying conditions is critical.

Practically, integrating such fusion networks into real-time systems could enhance both modality-based sensing and computational demanding operations. As AI continues to evolve, techniques like these could become fundamental in developing more responsive and adaptable detection systems in scenarios where standalone modalities might be insufficient.

Speculations on Future Developments

The methodology and comprehensive dataset proposed pave the way for further exploration into more adaptive fusion networks, particularly those that could benefit other high-level image processing tasks like semantic segmentation. Further research could extend into refining cooperative training strategies to better exploit high-level features and apply similar strategies within the field of video processing and 3D imaging.

In conclusion, this paper provides a valuable advancement in the field of multimodal image processing and object detection by introducing a target-aware dual adversarial framework. The comparative performance improvements exhibited in the experimental results corroborate the efficacy of such approaches, highlighting its potential benefits to a wide range of applications in AI. Future research is anticipated to build upon this work, exploring its integrations and potential beyond the scope of the current framework.