The detection of small objects in aerial images is a fundamental task in the
field of computer vision. Moving objects in aerial photography have problems
such as different shapes and sizes, dense overlap, occlusion by the background,
and object blur, however, the original YOLO algorithm has low overall detection
accuracy due to its weak ability to perceive targets of different scales. In
order to improve the detection accuracy of densely overlapping small targets
and fuzzy targets, this paper proposes a dynamic-attention scale-sequence
fusion algorithm (DASSF) for small target detection in aerial images. First, we
propose a dynamic scale sequence feature fusion (DSSFF) module that improves
the up-sampling mechanism and reduces computational load. Secondly, a x-small
object detection head is specially added to enhance the detection capability of
small targets. Finally, in order to improve the expressive ability of targets
of different types and sizes, we use the dynamic head (DyHead). The model we
proposed solves the problem of small target detection in aerial images and can
be applied to multiple different versions of the YOLO algorithm, which is
universal. Experimental results show that when the DASSF method is applied to
YOLOv8, compared to YOLOv8n, on the VisDrone-2019 and DIOR datasets, the model
shows an increase of 9.2% and 2.4% in the mean average precision (mAP),
respectively, and outperforms the current mainstream methods.

本文提出了一种适用于航拍图像中小目标检测的动态注意力尺度序列融合算法 (DASSF)，通过改进 YOLO 算法的能力以感知不同尺度的目标来提高密集重叠小目标和模糊目标的检测精度。实验结果表明，在 VisDrone-2019 和 DIOR 数据集上，将 DASSF 方法应用于 YOLOv8 时，与 YOLOv8n 相比，模型的平均精度 (mean average precision) 分别提高了 9.2% 和 2.4%，超过了当前主流方法。

DASSF：用于航空目标检测的动态注意力尺度序列融合

DASSF: Dynamic-Attention Scale-Sequence Fusion for Aerial Object  Detection

Effective detection of road hazards plays a pivotal role in road
infrastructure maintenance and ensuring road safety. This research paper
provides a comprehensive evaluation of YOLOv8, an object detection model, in
the context of detecting road hazards such as potholes, Sewer Covers, and Man
Holes. A comparative analysis with previous iterations, YOLOv5 and YOLOv7, is
conducted, emphasizing the importance of computational efficiency in various
applications. The paper delves into the architecture of YOLOv8 and explores
image preprocessing techniques aimed at enhancing detection accuracy across
diverse conditions, including variations in lighting, road types, hazard sizes,
and types. Furthermore, hyperparameter tuning experiments are performed to
optimize model performance through adjustments in learning rates, batch sizes,
anchor box sizes, and augmentation strategies. Model evaluation is based on
Mean Average Precision (mAP), a widely accepted metric for object detection
performance. The research assesses the robustness and generalization
capabilities of the models through mAP scores calculated across the diverse
test scenarios, underlining the significance of YOLOv8 in road hazard detection
and infrastructure maintenance.

在道路基础设施维护和保障道路安全中，有效检测道路危险是至关重要的。本研究评估了 YOLOv8，一种物体检测模型，在检测路面危险，如坑洞、下水道盖和人孔等方面的综合性能。通过与之前版本 YOLOv5 和 YOLOv7 的比较分析，强调了计算效率在各种应用中的重要性。本文探讨了 YOLOv8 的架构，并探索了旨在提高检测准确性的图像预处理技术，包括光照、道路类型、危险大小和类型的多样条件。此外，通过调整学习率、批量大小、锚点框大小和增强策略进行超参数调优实验，优化模型的性能。模型评估基于 Mean Average Precision (mAP)，这是一种广泛接受的物体检测性能指标。通过在多种测试场景下计算 mAP 分数，本研究评估了模型的稳健性和泛化能力，强调了 YOLOv8 在道路危险检测和基础设施维护中的重要性。