This study investigates the application of single and two-stage 2D-object
detection algorithms like You Only Look Once (YOLO), Real-Time DEtection
TRansformer (RT-DETR) algorithm for automated object detection to enhance road
safety for autonomous driving on Austrian roads. The YOLO algorithm is a
state-of-the-art real-time object detection system known for its efficiency and
accuracy. In the context of driving, its potential to rapidly identify and
track objects is crucial for advanced driver assistance systems (ADAS) and
autonomous vehicles. The research focuses on the unique challenges posed by the
road conditions and traffic scenarios in Austria. The country's diverse
landscape, varying weather conditions, and specific traffic regulations
necessitate a tailored approach for reliable object detection. The study
utilizes a selective dataset comprising images and videos captured on Austrian
roads, encompassing urban, rural, and alpine environments.

这项研究探讨了单阶段和两阶段的 2D 目标检测算法，例如 You Only Look Once (YOLO) 和 Real-Time DEtection TRansformer (RT-DETR) 算法在奥地利道路的自动化目标检测中的应用，以提高道路安全性。该研究利用奥地利道路上的图像和视频构建了选择性数据集，涵盖了城市、农村和高山环境，并针对奥地利道路的独特挑战进行了定制化的目标检测方法。

奥地利自动驾驶中基于 YOLO 和 DETR 的深度学习视觉模型初步定性观察

First qualitative observations on deep learning vision model YOLO and  DETR for automated driving in Austria

We propose a novel Attentional Scale Sequence Fusion based You Only Look Once
(YOLO) framework (ASF-YOLO) which combines spatial and scale features for
accurate and fast cell instance segmentation. Built on the YOLO segmentation
framework, we employ the Scale Sequence Feature Fusion (SSFF) module to enhance
the multi-scale information extraction capability of the network, and the
Triple Feature Encoder (TPE) module to fuse feature maps of different scales to
increase detailed information. We further introduce a Channel and Position
Attention Mechanism (CPAM) to integrate both the SSFF and TPE modules, which
focus on informative channels and spatial position-related small objects for
improved detection and segmentation performance. Experimental validations on
two cell datasets show remarkable segmentation accuracy and speed of the
proposed ASF-YOLO model. It achieves a box mAP of 0.91, mask mAP of 0.887, and
an inference speed of 47.3 FPS on the 2018 Data Science Bowl dataset,
outperforming the state-of-the-art methods. The source code is available at
this https URL

提出了一种新颖的基于注意力尺度序列融合的 You Only Look Once (YOLO) 框架（ASF-YOLO），该框架结合了空间和尺度特征，用于准确和快速的细胞实例分割。在 YOLO 分割框架基础上，采用尺度序列特征融合（SSFF）模块增强了网络的多尺度信息提取能力，并使用三重特征编码器（TPE）模块融合不同尺度的特征图，以增加详细信息。此外，引入了通道和位置注意机制（CPAM）来整合 SSFF 和 TPE 模块，重点关注信息丰富的通道和空间位置相关的小物体，以提高检测和分割性能。实验验证表明，提出的 ASF-YOLO 模型在两个细胞数据集上具有显着的分割准确性和速度。在 2018 年 Data Science Bowl 数据集上，它实现了 0.91 的框 mAP，0.887 的掩膜 mAP，并具有 47.3 FPS 的推理速度，优于现有的最先进方法。源代码可以在此 URL 找到。