Parking has been a common problem over several years in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, the aim of the study is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect videos during varying environmental conditions and frames from these videos were extracted to prepare the dataset. The frames in the dataset were manually labelled as there were no pre-labelled thermal images available. Vehicle detection with deep learning algorithms was implemented to perform multi-object detection. Multiple deep learning networks such as Yolo, Yolo-conv, GoogleNet, ReNet18 and ResNet50 with varying layers and architectures were evaluated on vehicle detection. ResNet18 performed better than other detectors which had an average precision of 96.16 and log-average miss rate of 19.40. The detected results were compared with a template of parking spaces to identify vehicle occupancy information. Yolo, Yolo-conv, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time and are suitable for real-time detection while Resnet50 was computationally expensive.

中文翻译：

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使用热像仪在开放式停车场中基于深度学习的车辆占用检测

多年来，停车已成为全球许多城市的普遍问题。寻找停车位会导致交通拥堵，沮丧并增加空气污染。空置停车位的信息将有助于减少交通拥堵和随后的空气污染。因此，研究的目的是利用深度学习来获得开放式停车场中的车辆占用率。使用热像仪收集各种环境条件下的视频，并从这些视频中提取帧以准备数据集。手动标记数据集中的帧，因为没有可用的预先标记的热图像。实施具有深度学习算法的车辆检测以执行多目标检测。多个深度学习网络，例如Yolo，Yolo-conv，GoogleNet，在车辆检测中评估了具有不同层和体系结构的ReNet18和ResNet50。ResNet18的性能优于其他检测器，后者的平均精度为96.16，对数平均未命中率为19.40。将检测到的结果与停车位模板进行比较，以识别车辆占用信息。Yolo，Yolo-conv，GoogleNet和ResNet18是计算效率高的检测器，其处理时间更少，适用于实时检测，而Resnet50在计算上却很昂贵。