The success of autonomous navigation relies on robust and precise vehicle
recognition, hindered by the scarcity of region-specific vehicle detection
datasets, impeding the development of context-aware systems. To advance
terrestrial object detection research, this paper proposes a native vehicle
detection dataset for the most commonly appeared vehicle classes in Bangladesh.
17 distinct vehicle classes have been taken into account, with fully annotated
81542 instances of 17326 images. Each image width is set to at least 1280px.
The dataset's average vehicle bounding box-to-image ratio is 4.7036. This
Bangladesh Native Vehicle Dataset (BNVD) has accounted for several
geographical, illumination, variety of vehicle sizes, and orientations to be
more robust on surprised scenarios. In the context of examining the BNVD
dataset, this work provides a thorough assessment with four successive You Only
Look Once (YOLO) models, namely YOLO v5, v6, v7, and v8. These dataset's
effectiveness is methodically evaluated and contrasted with other vehicle
datasets already in use. The BNVD dataset exhibits mean average precision(mAP)
at 50% intersection over union (IoU) is 0.848 corresponding precision and
recall values of 0.841 and 0.774. The research findings indicate a mAP of 0.643
at an IoU range of 0.5 to 0.95. The experiments show that the BNVD dataset
serves as a reliable representation of vehicle distribution and presents
considerable complexities.

自主导航的成功依赖于稳健和精确的车辆识别，但由于特定地区车辆检测数据集的稀缺性，阻碍了上下文感知系统的发展。为了推进地面物体检测研究，本文提出了一个用于孟加拉国常见车辆类别的本土车辆检测数据集。该数据集包括 17 个不同的车辆类别，完全注释了 17326 张图像中的 81542 个实例，每个图像宽度至少为 1280px。结果表明，该数据集在意外场景上更加稳健，对地理、光照、车辆尺寸和朝向等因素进行了充分考虑。在考察本土车辆检测数据集的背景下，本文通过四种连续的 You Only Look Once (YOLO) 模型，即 YOLO v5、v6、v7 和 v8，对数据集的有效性进行了全面评估，并与已经使用的其他车辆数据集进行了对比。实验结果表明，BNVD 数据集在 50% 交并比下的平均精度为 0.848，相应的精确度和召回率分别为 0.841 和 0.774。研究发现表明，在 0.5 到 0.95 的交并比范围内，mAP 为 0.643。实验表明，BNVD 数据集可靠地代表了车辆分布，并呈现出相当复杂性。

野外条件下的孟加拉国本土车辆检测

Bangladeshi Native Vehicle Detection in Wild

Blackgrass (Alopecurus myosuroides) is a competitive weed that has
wide-ranging impacts on food security by reducing crop yields and increasing
cultivation costs. In addition to the financial burden on agriculture, the
application of herbicides as a preventive to blackgrass can negatively affect
access to clean water and sanitation. The WeedScout project introduces a
Real-Rime Autonomous Black-Grass Classification and Mapping (RT-ABGCM), a
cutting-edge solution tailored for real-time detection of blackgrass, for
precision weed management practices. Leveraging Artificial Intelligence (AI)
algorithms, the system processes live image feeds, infers blackgrass density,
and covers two stages of maturation. The research investigates the deployment
of You Only Look Once (YOLO) models, specifically the streamlined YOLOv8 and
YOLO-NAS, accelerated at the edge with the NVIDIA Jetson Nano (NJN). By
optimising inference speed and model performance, the project advances the
integration of AI into agricultural practices, offering potential solutions to
challenges such as herbicide resistance and environmental impact. Additionally,
two datasets and model weights are made available to the research community,
facilitating further advancements in weed detection and precision farming
technologies.

该研究介绍了一个实时黑草分类与制图系统（RT-ABGCM），使用人工智能算法处理图像数据，推断黑草密度并覆盖两个成熟阶段，以实现精准的杂草管理，并探索了加速边缘端的 YOLOv8 和 YOLO-NAS 模型在农业实践中的应用，提供 AI 在农业实践中的潜在解决方案。此外，研究将两个数据集和模型权重提供给研究社区，促进杂草检测和精准农业技术的进一步发展。