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1. Deep learning-based object detection algorithms have become a popular method for processing moving images captured from drones, with potential applications in surveillance, visual navigation, object detection, and sensors-based obstacle avoidance planning.

2. The paper provides a comprehensive review of the state of the art deep learning based object detection algorithms and analyzes recent contributions to low altitude UAV datasets, which have received relatively less attention in the literature compared to standard or remote-sensing based datasets.

3. The paper categorizes object detectors into single-stage, two-stage, and advanced stages and summarizes their comparative performances by analyzing results on low-altitude benchmark datasets. It also lists various research gaps and challenges for improving the performance of object detection and classification in UAV datasets.

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