Arbitrary-oriented object detection is vital for improving UAV sensing and has promising applications. However, challenges persist in detecting objects under extreme conditions like low-illumination and strong occlusion. Cross-modal feature fusion enhances detection in complex environments but current methods do not adequately learn the features of each modality for the current environment, resulting in degraded performance. To tackle this, we propose the CRSIOD network that effectively learns diverse sensor image features to capture distinct scenarios and target characteristics. Firstly, we design an illumination perception module to guide the object detection network in performing various feature processing tasks. Secondly, to leverage the respective advantages of two modalities and mitigate their negative impacts, we introduce an uncertainty aware module to quantify the uncertainties present in each modality as weights to motivate the network to learn in a direction favorable for optimal object detection. Moreover, in the object detection network, we design a two-stream backbone network based on the attention mechanism to enhance the learning of difficult samples, utilize the CMAFF module to fully extract the shared and complementary features between the two modalities, and design a three-branch feature enhancement network to enhance the learning of the three modal features separately. Finally, to optimize detection results, we design light perception non-maximum suppression and improve the horizontal detection head to a rotating one to preserve object orientation. We evaluate the proposed method CRSIOD on the Drone Vehicle dataset of public UAV aerial images. Compared with the existing commonly used methods, CRSIOD achieves state-of-the-art detection performance.

Effective detection of small targets plays a pivotal role in infrared (IR) search and track applications for modern military defense or attack. However, IR small targets are very difficult to detect because of their weak brightness, small size, and lack of shape, structure, texture, and other information elements. In order to simultaneously satisfy the robustness and timeliness of target detection, inspired by density peak clustering and the human visual system, an idea combining an improved density peak global search and local contrast calculation is proposed. First, the positions of candidate targets are determined in the preprocessed image using the improved density peak global search method (IDPGSM). Second, the saliency map is obtained using the double-weights enhanced local contrast method (DWELCM) for the candidate target neighborhood. Finally, adaptive threshold segmentation is used to detect IR small targets. Through comprehensive analysis of five evaluation indicators, the experimental results on seven real sequences and three hundred IR images of different scenes that the proposed method has better detection performance compared with six baseline methods. It can quickly and accurately determine the small target position in the case of severe background clutter and noise interference.

With the rapid advancements in deep learning technology, various deep learning-based object detection algorithms have found extensive applications in UAV-related tasks. However, motivated by the fact that current object detection algorithms for unimodal aerial remote sensing images (ARSI) fail to achieve around-the-clock object detection. To tackle this, we propose an around-the-clock object detection algorithm YOLOFIV that fuses infrared and visible features. First, we design a dual-stream backbone network based on the attention mechanism to adequately extract the features of both modalities. Moreover, the ECA attention mechanism is integrated into the feature enhancement network to amplify attention toward challenging detection scenarios. Finally, we improve the horizontal detection head to a rotating one to preserve object orientation. We evaluate the proposed method YOLOFIV on the widely used Drone Vehicle dataset, YOLOFIV achieves an accuracy of 64.71% (in terms of $mAP_{0.5}$ ), accuracy improvement of 8.32% over baseline bimodal model, similar performance to UACMD designed for ARSI object detection but with 92.35% reduction in parameter count, and 17.87 times speedup. The results show that our approach achieves round-the-clock object detection while maintaining a favorable accuracy-speed trade-off.

Ship detection is a significant and challenging task in remote sensing. At present, anchor-based ship detectors have achieved remarkable results, but they require introducing additional parameters and their performance is easily affected by the size of anchor boxes. In this paper, we propose an anchor-free rotated detector (OFCOS) for ship detection based on FCOS, which can be trained end-to-end. Specifically, a feature-enhanced feature pyramid network (FE-FPN) is proposed, in which the structure of the feature pyramid is optimized and an attention mechanism is introduced during fusion to enhance the significance of object features. Then, to better describe the orientation of objects, a regression branch with orientation characterization capability is constructed, and a center-to-corner bounding box prediction strategy is used to improve the accuracy of object localization. Moreover, the calculation of center-ness is optimized so that the assignment of center weights is orientation-aware and adaptive to down-weight low-quality predictions. A new remote sensing ship dataset, named RS-Ship, is constructed to further verify the effectiveness and robustness of OFCOS. Our experiments show that OFCOS achieves AP values of 91.07% and 97.05% on the publicly available dataset HRSC2016 and our self-built RS-Ship dataset, respectively, which are 13.01% and 9.84% higher than FCOS. OFCOS outperforms other mainstream detection methods in terms of both detection speed and detection accuracy.

Camouflaged object detection (COD) aims to identify objects that are perfectly concealed in their surroundings and has attracted increasing attention in recent years. The challenge with COD is the intrinsic similarity between camouflaged objects and background, as well as the weak boundary that often accompanies camouflaged objects. In this paper, a Progressive Refinement Network called PRNet is proposed based on human perception of camouflaged images. Specifically, we develop a position-aware module to roughly locate the position of camouflaged objects by reverse-guiding with high-level semantic information. Moreover, an edge-guided fusion module is designed to simultaneously refine the boundaries and regions of camouflaged objects by using edge features as a guide in cross-level feature fusion. Benefited from the utility of the above two modules, our PRNet is able to identify camouflaged objects accurately and quickly. Numerous experiments on four widely used benchmark datasets demonstrate that the proposed PRNet is an efficient COD model, outperforming 14 state-of-the-art algorithms significantly and running at a real-time

Identifying challenging samples in real-time aerial images captured by UAVs is currently a difficult task. Lightweight network designs often utilize lightweight modules or employ methods such as network pruning, quantization, and distillation for real-time detection on embedded devices. However, these approaches are inadequate for complex background aerial images. In this paper, we propose a real-time object detection algorithm specifically designed for complex aerial images, named MINIAOD. First, we use Ghost convolution to build a lightweight backbone network, which improves the detection speed without compromising the feature extraction capability. Additionally, we utilize the GSConv module to construct a feature enhancement network and develop the GSC3ECA module to improve the network’s learning capability for challenging targets, while also decreasing computational complexity. To boost the network’s focus on small and medium-sized targets and mitigate interference from complex backgrounds, we incorporate the ECA attention mechanism in the small and medium-sized target detection branch to elevate the focus on difficult-to-detect samples. Finally, we design the rotation detection head to characterize the target directionality of aerial images. We perform extensive experiments on multiple publicly available datasets and the proposed method demonstrates a favorable balance between accuracy and speed when compared to the current leading models.