论文标题
一个轻巧，准确的类似YOLO的网络，用于空中图像中的小目标检测
A lightweight and accurate YOLO-like network for small target detection in Aerial Imagery
论文作者
论文摘要
尽管对自动对象检测取得的深入学习表演的突破性，但小目标检测仍然是一个具有挑战性的问题，尤其是在查看适合移动或边缘应用程序的快速准确解决方案时。在这项工作中，我们提出了Yolo-S，这是一个简单，快速有效的网络，用于小型目标检测。该体系结构利用了基于DarkNet20的小型提取器，并通过旁路和串联启动连接，并重新构造式汇总层，以减轻消失的梯度问题，促进跨网络的重复使用并结合低级位置信息，并将其与更有意义的高级高级信息相结合。为了验证Yolo-S的性能，我们构建了“ Aires”，这是一种从欧洲获取的直升机图像中检测的新型数据集，并在Aires和Vedai数据集上进行了实验，用四个基线探测器对此进行了基准测试。此外，为了在处理转移学习策略时有效处理数据不足和域间隙问题，我们在基于dotav2和vedai的组合数据集上介绍了一项过渡性学习任务，并证明可以提高与从Coco数据转移更一般特征的更一般特征相对于从COCO数据转移的整体准确性。 Yolo-S比Yolov3快25％至50％，仅比Tiny-Yolov3慢15-25％，在广泛的实验中的准确性方面，Yolov3的表现也优于Yolov3。在SARD数据集上进行的进一步模拟也证明了其适用于诸如搜索和救援操作之类的不同情况。此外，Yolo-S的参数大小减少了87％，而Yolov3的几乎一半触及了，这使得实际上是低功率工业应用的部署。
Despite the breakthrough deep learning performances achieved for automatic object detection, small target detection is still a challenging problem, especially when looking at fast and accurate solutions suitable for mobile or edge applications. In this work we present YOLO-S, a simple, fast and efficient network for small target detection. The architecture exploits a small feature extractor based on Darknet20, as well as skip connection, via both bypass and concatenation, and reshape-passthrough layer to alleviate the vanishing gradient problem, promote feature reuse across network and combine low-level positional information with more meaningful high-level information. To verify the performances of YOLO-S, we build "AIRES", a novel dataset for cAr detectIon fRom hElicopter imageS acquired in Europe, and set up experiments on both AIRES and VEDAI datasets, benchmarking this architecture with four baseline detectors. Furthermore, in order to handle efficiently the issue of data insufficiency and domain gap when dealing with a transfer learning strategy, we introduce a transitional learning task over a combined dataset based on DOTAv2 and VEDAI and demonstrate that can enhance the overall accuracy with respect to more general features transferred from COCO data. YOLO-S is from 25% to 50% faster than YOLOv3 and only 15-25% slower than Tiny-YOLOv3, outperforming also YOLOv3 in terms of accuracy in a wide range of experiments. Further simulations performed on SARD dataset demonstrate also its applicability to different scenarios such as for search and rescue operations. Besides, YOLO-S has an 87% decrease of parameter size and almost one half FLOPs of YOLOv3, making practical the deployment for low-power industrial applications.
