GEM: Glare or Gloom, I Can Still See You -- End-to-End Multimodal Object Detection
This work addresses robustness in object detection for applications like self-driving vehicles and human-robot interaction, though it is incremental as it builds on existing multimodal fusion approaches.
The paper tackles the problem of robust object detection under harsh lighting conditions and asymmetric sensor degradation by developing a multimodal 2D object detector with sensor-aware feature fusion strategies, showing that it outperforms state-of-the-art methods on the FLIR-Thermal dataset and achieves promising results on SUNRGB-D.
Deep neural networks designed for vision tasks are often prone to failure when they encounter environmental conditions not covered by the training data. Single-modal strategies are insufficient when the sensor fails to acquire information due to malfunction or its design limitations. Multi-sensor configurations are known to provide redundancy, increase reliability, and are crucial in achieving robustness against asymmetric sensor failures. To address the issue of changing lighting conditions and asymmetric sensor degradation in object detection, we develop a multi-modal 2D object detector, and propose deterministic and stochastic sensor-aware feature fusion strategies. The proposed fusion mechanisms are driven by the estimated sensor measurement reliability values/weights. Reliable object detection in harsh lighting conditions is essential for applications such as self-driving vehicles and human-robot interaction. We also propose a new "r-blended" hybrid depth modality for RGB-D sensors. Through extensive experimentation, we show that the proposed strategies outperform the existing state-of-the-art methods on the FLIR-Thermal dataset, and obtain promising results on the SUNRGB-D dataset. We additionally record a new RGB-Infra indoor dataset, namely L515-Indoors, and demonstrate that the proposed object detection methodologies are highly effective for a variety of lighting conditions.