融合多尺度注意力机制和改进特征融合的轻量化水面小目标检测模型

Lightweight water surface small object detection model with multi-scale attention mechanism and improved feature fusion

摘要

在复杂水面场景下,针对小目标检测精度较低、漏检率高和计算资源有限的问题,提出一种融合多尺度注意力机制和改进特征融合的轻量化水面小目标检测模型。根据中心度理论,设计新的主干网络,利用多尺度注意力机制增强模型的特征提取能力,利用部分卷积从减少特征图冗余的角度改进颈部网络,有效降低模型计算量。使用大型可分离核注意力模块改进快速空间金字塔池化模块,提升模型的特征融合能力。实验结果表明,与其他模型相比,本文模型的检测精度高、漏检率低、参数量少。

Abstract

In complex water surface scenarios, addressing the issues of low detection accuracy, high missed detection rates, and limited computational resources for small target detection, this paper proposes a lightweight water surface small object detection model with multi-scale attention mechanism and improved feature fusion. Based on centerness theory, a new backbone network is designed, leveraging the multi-scale attention mechanism to enhance the model's feature extraction capabilities. Partial convolution is used to improve the neck network by reducing feature map redundancy, effectively lowering the model's computational load. A large separable kernel attention module is employed to improve the spatial pyramid pooling module, enhancing the model's feature fusion ability. Experimental results demonstrate that, compared to other models, the proposed model achieves higher detection accuracy, lower missed detection rates, and fewer parameters.

关键词

小目标检测 / 特征融合 / 多尺度注意力机制 / 特征图冗余

Key words

small target detection / feature fusion / multi-scale attention mechanism / feature map redundancy

参考文献

[1] GIRSHICK R. Fast R—CNN[C]// Proceedings of the IEEE International Conference on Computer Vision (ICCV). Boston: IEEE, 2015: 1440-1448.

[2] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real—time object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 779-788.

[3] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector[C]// Proceedings of the 14th European Conference on Computer Vision (ECCV). Berlin: Springer, 2016: 21-37.

[4] REN S Q, HE K M, GIRSHICK R, et al. Faster R—CNN: towards real—time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.

[5] TAO Kong, YAO Anbang, CHEN Yurong, et al. HyperNet: Towards accurate region proposal generation and joint object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 845-853.

[6] LIN T Y, DOLLAR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// Proceedings of the 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 936-944.

[7] CHEN C Y, LIU M Y, TUZEL O, et al. R—CNN for small object detection[C]// Proceedings of the 13th Asian Conference on Computer Vision. Berlin: Springer, 2016: 214-230.

[8] 戚玲珑, 高建瓴. 基于改进YOLOv7的小目标检测[J]. 计算机工程, 2023, 49(1): 41-48.

[9] QI Linglong, GAO Jianling. Small target detection based on improved YOLOv7[J]. Computer Engineering, 2023, 49(1): 41-48.

[10] LIN Feng, HOU Tian, JIN Qiannan, et al. Improved YOLO based detection on algorthm for floating debris in waterway[J]. Entropy, 2021, 23(9): 1111.

[11] 王林, 汪钰婷. 基于加强特征融合的轻量化船舶目标检测[J]. 计算机系统应用, 2023, 32(2): 288-294.

[12] WANG Lin, WANG Yuting. Lightweight ship target detection based on enhanced feature fusion[J]. Computer System Applications, 2023, 32(2): 288-294.

[13] OUYANG Daliang, HE Su, ZHAN Jian, et al. Efficient multi—scale attention moudle with cross—spatial learning[C]// Proceedings of the IEEE Interrnational Conference on Acoustics Speech and Signal Processing (ICASSP). Rhodes Island: IEEE, 2023: 1-5.

[14] CHEN J R, KAO S, HE H, et al. Run, don't walk: chasing higher flops for faster neural networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver: IEEE, 2023: 12021-12031.

[15] LAU K W, POL M, REHMAN Y A U. Large separable kernel attention: rethinking the large kernel attention design in CNN[J]. Expert Systems with Applications, 2024, 236: 121352.

[16] ZHOU Zhiguo, SUN Jiaen, YU Jiabao, et al. An image—based benchmark dataset and a novel object detector for water surface object detection[J]. Frontiers in Neurorobotics, 2021, 15: 723336.

[17] HU Jie, SHEN Li, SUN Gang. Squeeze—and—excitation networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway: IEEE, 2018: 7132-7141.

[18] WOO S H, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]// Proceedings of the 15th European Conference on Computer Vision (ECCV). Munich: Springer, 2018: 3-19.

基金资助

河北省高等学校科学技术研究重点资助项目(ZD2018043)

河北省教育科学规划课题一般资助课题项目(2303121)

河北地质大学博士基金资助项目(BQ2017045)