Drone-based RGBT person detection promotes various applications such as search and rescue due to its maneuverability. While existing research predominantly concentrates on refining fusion strategies and bolstering learning mechanisms for small objects, the pervasive yet unique occlusion challenge in drone-based RGBT settings remains inadequately addressed. In this work, we address the unique challenge of occlusion in the context of RGBT small object detection, particularly emphasizing its vulnerability and the distinct characteristics it exhibits across different modalities. We propose AODet, a novel Anti-Occlusion Detector meticulously crafted to tackle the challenges posed by occlusion in drone-based RGBT object detection. Our proposed approach significantly improves the detection performance of RGBT small objects, surpassing strong baselines on two large-scale datasets, VTUAV-det and RGBTDronePerson, by 1.30 points and 2.24 points in mAPsand ${\text{mAP}}_{50}^{{\text{tiny}}}$, respectively.

Nighttime aerial tracking is an indispensable step towards around-the-clock real-world applications. However, RGBbased tracking algorithms face significant challenges at night due to their vulnerability to illumination. Observing that different feature channels have varying sensitivity to illumination, we propose to decouple the representation for illuminationsensitive and illumination-insensitive embeddings. We devise a Nighttime aerial tracking scheme via Decouple Representations, termed NiDR, where the Illumination-Invariant Embedding (IIE) module and the Illumination-Sensitive Embedding (ISE) module are designed to decouple representations. We achieve this semantic decoupling by utilizing a pair of normlight and low-light images and regulating the reconstruction and consistency relations between features. Experiments on UAVDark135 exhibit the remarkable performance of NiDR under challenging nighttime scenarios, surpassing the secondbest competitor by a large margin of 3.1% on precision.

Vanilla aerial trackers exhibit sensitivity to low-light conditions (e.g., nighttime aerial tracking scenario). To mitigate this, existing methods incorporate the light enhancement method as a preprocessing for aerial tracking. Despite the advancements, these approaches are restricted to the disparity in task objectives between the enhancer and tracker. Motivated by the observation that feature channels exhibit varying sensitivity to illumination, we propose to decouple the feature representation into two distinct parts: 1) illumination-invariant feature embedding and 2) illumination-sensitive feature embedding. The former, realized by the illumination invariant embedding (IIE) module, enhances features that remain invariant to illumination changes. Meanwhile, the latter, facilitated by the illumination sensitive embedding (ISE) module, aims to mitigate the negative impact of illumination-sensitive features on tracking performance. Building upon this decoupling strategy, we introduce NiDR, a simple yet effective nighttime aerial tracker. The proposed NiDR exhibits strong performance on three nighttime aerial tracking benchmarks (i.e., UAVDark135, NAT2021, and DarkTrack2021). Notably, it outperforms previous competitors by large margins, e.g., 3.1 points on the UAVDark135 and 2.0 points on the Darktrack2021 in terms of precision for nighttime scenarios.

Thermal infrared (TIR) object detection plays a crucial role in diverse around-the-clock applications, such as search and rescue operations and wildlife protection. Achieving rapid and robust detection of small objects from an aerial perspective is particularly significant in these scenarios. However, the task is compounded by two interrelated challenges, rendering it even more tricky. For one, small objects only occupy a few pixels and contain limited information. For another, TIR sensors are typically low-resolution (LR) due to inherent challenges associated with the imaging mechanism of the TIR spectrum. In contrast, high-resolution (HR) RGB sensors are readily available due to their cost-effectiveness and widespread application. Recognizing the importance of HR information, especially in the context of small object detection, we propose a cross-modality high-resolution knowledge distillation framework (CMHRD), which leverages knowledge from the HR-RGB modality and provides a novel strategy for TIR small object detection. The proposed framework introduces three key components: a super-resolution generative distillation loss for cross-modal high-resolution representation learning, a cross-modality affinity distillation loss to extract scene-level cross-modality information, and a response distillation loss aimed at mimicking the HR prediction. To facilitate research on small object detection with HR-RGB and LR-TIR data, we have curated and annotated two datasets, namely NOAA-Seal and VTUAV-det-small. Experimental results on the NOAA-Seal demonstrate that CMHRD yields significant improvements, achieving a remarkable 6.39 mAP50 increase over a strong baseline without introducing additional computational cost during inference. Experiments on single-category dataset VTUAV-det-small and multi-category dataset RTDOD also show consistent improvements brought by CMHRD. The project is available at https://github.com/NNNNerd/CMHRD.

Tracking arbitrary objects in aerial images presents formidable challenges to existing trackers. Among these challenges, the large scale variation and arbitrary geometry shape of visual targets are pronounced, resulting in two-fold mismatch issues between the feature receptive field and the tracking target. For one, there is a mismatch between the prior receptive field center and arbitrary-shaped targets. For another, the single receptive field mismatches the significantly scale-varied targets in the aerial imagery. To handle these challenges, we propose to Achieve precise Aerial tracking with receptive field Alignment, dubbed A3Track. The proposed A3Track is comprised of two modules: a Receptive Field Alignment (RFA) module and a Pyramid Receptive Field (PRF) module. First of all, we transform and update the receptive field center progressively, which drives the feature sampling location onto the targets’ main body, thus gradually yielding precise feature representation for arbitrary-shaped targets. We term this progressively updating process as the Receptive Field Alignment. Moreover, the PRF module constructs a set of pyramid features for the target, providing a multi-scale receptive field to handle the large scale variation of tracking objects. On four benchmarks, the new tracker A3Track achieves leading performance compared with existing methods and shows consistent improvements over baselines. The project is available at: https://chnleixu.github.io/A3Track-web/.

This paper presents a multi-scale Best-Buddies Similarity (BBS) method for matching objects in the UAV imagery. More precisely, we first extract proposed regions from the target image by Selective Search, then take a number of proposed regions with the highest confidence scores as templates and finally apply templates to the original BBS method and select the coordinate of the region with the highest overlap rate as the exact position of the query object in the target image. Experimental results on real data show the effectiveness of the proposed method.