Lee Young-Woon, Kim Byung-Gyu
Department of Computer Engineering, Sunmoon University, Asan, Republic of Korea.
Division of Artificial Intelligence Engineering, Sookmyung Women's University, Seoul, Republic of Korea.
Heliyon. 2024 Jun 19;10(12):e32931. doi: 10.1016/j.heliyon.2024.e32931. eCollection 2024 Jun 30.
Recently, with the remarkable development of deep learning technology, achievements are being updated in various computer vision fields. In particular, the object recognition field is receiving the most attention. Nevertheless, recognition performance for small objects is still challenging. Its performance is of utmost importance in realistic applications such as searching for missing persons through aerial photography. The core structure of the object recognition neural network is the feature pyramid network (FPN). You Only Look Once (YOLO) is the most widely used representative model following this structure. In this study, we proposed an attention-based scale sequence network (ASSN) that improves the scale sequence feature pyramid network (ssFPN), enhancing the performance of the FPN-based detector for small objects. ASSN is a lightweight attention module optimized for FPN-based detectors and has the versatility to be applied to any model with a corresponding structure. The proposed ASSN demonstrated performance improvements compared to the baselines (YOLOv7 and YOLOv8) in average precision () of up to 0.6%. Additionally, the AP for small objects ( ) showed also improvements of up to 1.9%. Furthermore, ASSN exhibits higher performance than ssFPN while achieving lightweightness and optimization, thereby improving computational complexity and processing speed. ASSN is open-source based on YOLO version 7 and 8. This can be found in our public repository: https://github.com/smu-ivpl/ASSN.git.
最近，随着深度学习技术的显著发展，各个计算机视觉领域的成果不断更新。特别是，目标识别领域受到了最多关注。然而，小目标的识别性能仍然具有挑战性。在通过航空摄影寻找失踪人员等实际应用中，其性能至关重要。目标识别神经网络的核心结构是特征金字塔网络（FPN）。“你只看一次”（YOLO）是遵循这种结构使用最广泛的代表性模型。在本研究中，我们提出了一种基于注意力的尺度序列网络（ASSN），它改进了尺度序列特征金字塔网络（ssFPN），提高了基于FPN的小目标检测器的性能。ASSN是一种为基于FPN的检测器优化的轻量级注意力模块，具有应用于任何具有相应结构模型的通用性。与基线模型（YOLOv7和YOLOv8）相比，所提出的ASSN在平均精度（mAP）上的性能提升高达0.6%。此外，小目标的平均精度（APs）也显示出高达1.9%的提升。此外，ASSN在实现轻量级和优化的同时，表现出比ssFPN更高的性能，从而提高了计算复杂度和处理速度。ASSN基于YOLO版本7和8开源。可在我们的公共存储库中找到：https://github.com/smu-ivpl/ASSN.git。
