With the rapid development of object detection technology for unmanned aerial vehicles (UAVs), it is convenient to collect data from UAV aerial photographs. They have a wide range of applications in several fields, such as monitoring, geological exploration, precision agriculture, and disaster early warning. In recent years, many methods based on artificial intelligence have been proposed for UAV object detection, and deep learning is a key area in this field. Significant progress has been achieved in the area of deep-learning-based UAV object detection. Thus, this paper presents a review of recent research on deep-learning-based UAV object detection. This survey provides an overview of the development of UAVs and summarizes the deep-learning-based methods in object detection for UAVs. In addition, the key issues in UAV object detection are analyzed, such as small object detection, object detection under complex backgrounds, object rotation, scale change, and category imbalance problems. Then, some representative solutions based on deep learning for these issues are summarized. Finally, future research directions in the field of UAV object detection are discussed.

Multi-UAV area coverage path planning is a challenging and important task in the field of multi-robots. To achieve efficient and complete coverage in grid-based environments with obstacles and complex boundaries, a multi-UAV area coverage path planning method based on an improved Deep Q-Network (DQN) is proposed in this paper. In the proposed method, a map preprocessing technique based on Depth-First Search (DFS) is introduced to automatically detect and remove unreachable areas. Subsequently, to achieve a reasonable task allocation, the Divide Areas based on Robots’ initial Positions (DARP) algorithm is utilized. In the path planning stage, an enhanced Dueling DQN reinforcement learning architecture is employed by introducing action encoding and prioritized experience replay mechanisms, which improves both training efficiency and policy quality. Moreover, a reward function specifically designed for complete coverage tasks is proposed, effectively reducing redundant visits and mitigating path degradation. Extensive experiments conducted on several benchmark maps show that the proposed method outperforms traditional DQN, Boustrophedon path planning, and Spanning Tree Coverage (STC) methods in terms of coverage rate, redundancy rate, and path length.

Autonomous exploration in unknown environments is a critical capability for multi-UAV systems. However, existing methods often suffer from unbalanced task allocation, low exploration efficiency, and unstable paths, especially in large-scale and complex scenarios. To address these challenges, this paper presents an adaptive coordination exploration approach for multi-UAV systems. In the proposed approach, dynamic region allocation, entropy-guided local planning, and direction-consistent frontier selection are integrated to achieve efficient and collaborative exploration. The system first partitions the environment adaptively based on workload and regional complexity. It then prioritizes high-information-value areas for exploration. Directional constraints are further applied to improve path continuity and reduce turning redundancy. Extensive experiments in indoor maze and pillar environments, and outdoor forest and urban environments demonstrate that the proposed approach outperforms state-of-the-art baselines. Furthermore, ablation studies validate the necessity and complementarity of each module. This work provides a practical and efficient solution for multi-UAV exploration in structured and unstructured environments.

Indoor layout estimation is a critical aspect of indoor scene understanding, aiming to recover and reconstruct the geometric structure information of indoor spaces by analyzing images or depth data. The indoor layout estimation is a challenging task, due to the complexity of the indoor environment, including the unstructured geometric construction and complex illumination conditions. To address these issues, an improved fuzzy decision and geometric inference based indoor layout estimation model from RGB-D images is proposed in this paper. In the proposed model, to address the challenges of fixed plane detection thresholds, missing wall planes, and depth data loss due to transparent or reflective materials, three main improved modules are presented, namely the fuzzy decision-based threshold adjustment module (FDTA), the region growing-based wall supplement module (RGWS), and the geometric inference-based depth completion module (GIDC). The FDTA module is used to optimize the plane detection results based on the initial plane detected of the wall and floor to improve the accuracy and robustness of layout estimation. Then, the RGWS module supplements missing wall planes in the preliminary detection results, while the GIDC module completes missing depth information due to transparency or reflectivity in the input images. Experimental results show that the proposed method significantly improves the accuracy and robustness of indoor layout estimation, providing a reliable and efficient solution for complex indoor scenes.