This work presents a neural network model capable of recognizing small and tiny objects in thermal images collected by unmanned aerial vehicles. Our model consists of three parts, the backbone, the neck, and the prediction head. The backbone is developed based on the structure of YOLOv5 combined with the use of a transformer encoder at the end. The neck includes a BI-FPN block combined with the use of a sliding window and a transformer to increase the information fed into the prediction head. The prediction head carries out the detection by evaluating feature maps with the Sigmoid function. The use of transformers with attention and sliding windows increases recognition accuracy while keeping the model at a reasonable number of parameters and computation requirements for embedded systems. Experiments conducted on public dataset VEDAI and our collected datasets show that our model has a higher accuracy than state-of-the-art methods such as ResNet, Faster RCNN, ComNet, ViT, YOLOv5, SMPNet, and DPNetV3. Experiments on the embedded computer Jetson AGX show that our model achieves a real-time computation speed with a stability rate of over 90%.

本研究提出了一种神经网络模型，能够在无人机收集的热像图中识别小型和超小型目标。模型结构包括骨干网络、中间层和预测头，其中骨干网络基于YOLOv5结构，结尾使用了Transformer编码器。中间层采用了BI-FPN块以及滑动窗口和Transformer增加预测头的信息输入。预测头通过Sigmoid函数对特征图执行检测。Transformer的注意力和滑动窗口的使用提高了识别准确性，同时保持了模型在嵌入式系统中的合理参数和计算需求。实验在公共数据集VEDAI和自己收集的数据集上进行，结果表明，与ResNet、Faster RCNN、ComNet、ViT、YOLOv5、SMPNet和DPNetV3等最先进方法相比，本模型具有更高的准确度。在嵌入式计算机Jetson AGX上的实验表明，本模型实现了实时计算，并具有超过90%的稳定性率。

无人机热图中使用深度学习进行目标检测