Improved Deciduous Tree Nest Detection Method Based on YOLOv5s

doi:10.3969/j.issn.1006-2475.2024.08.005

Abstract

Abstract: To address the difficulty of detecting small bird nest targets in complex backgrounds， an improved YOLOv5s network architecture named YOLOv5s-nest is proposed. YOLOv5s-nest incorporates several enhancements: a refined attention mechanism called Bi-CBAM is inserted into the Backbone to effectively enhance the network’s perception of small targets; the SDI structure is introduced into the Neck to integrate more hierarchical feature maps and higher-level semantic information; the InceptionNeXt structure is inserted into the Neck to improve the model's performance and computational efficiency; and in the detection head， ordinary convolutions are replaced with PConv to efficiently extract spatial features and enhance detection efficiency. The experimental results show that the average precision of the improved model reached 89.1%， representing an increase of 6.8 percentage points compared to the original model.

Key words: deciduous trees, nest recognition, UAV image, deep learning, object detection

CLC Number:

TP391

CHENG Meng, LI Hao. Improved Deciduous Tree Nest Detection Method Based on YOLOv5s[J]. Computer and Modernization, 2024, 0(08): 24-29.

References

［1］马红艳，张喆. 昆明不同类型城市公园绿地鸟类多样性分析［J］. 现代园艺， 2023，46（12）:161-164.
［2］袁荣斌，俞长好，韦铭，等. 江西省资溪县鸟类多样性调查［J］. 南方林业科学， 2023，51（1）:74-78.
［3］佟富春，陆红璇，袁千允，等. 华南农业大学校园鸟类多样性分析［J］. 福建林业科技， 2023，50（2）:70-76.
［4］宋林，姚艳. 鸟类的野外调查和监测技术［J］. 林业与生态， 2022（4）:36-37.
［5］李新平，杨向明. 鸟类数量调查方法应用探讨［J］. 山西林业科技， 2014，43（1）:63-64.
［6］钱磊，李言阔，李佳琦，等. 利用红外相机技术调查江西省齐云山国家级自然保护区鸟类和兽类多样性［J］. 生态与农村环境学报， 2022，38（7）:890-896.
［7］王桢. 基于视频图像处理技术的输电线路杆塔鸟巢识别［D］. 北京:华北电力大学， 2013.
［8］段旺旺，唐鹏，金炜东，等. 基于关键区域HOG特征的铁路接触网鸟巢检测［J］. 中国铁路， 2015（8）:73-77.
［9］ WU X， YUAN P， PENG Q， et al. Detection of bird nests in overhead catenary system images for high-speed rail［J］. Pattern Recognition， 2016，51:242-254.
［10］师飘. 输电线路上鸟巢的检测算法研究［D］. 北京:北京交通大学， 2017.
［11］徐晶，韩军，童志刚，等. 一种无人机图像的铁塔上鸟巢检测方法［J］. 计算机工程与应用， 2017，53（6）:231-235.
［12］祁婕，焦良葆. 改进SSD的输电铁塔鸟窝检测［J］. 计算机系统应用， 2020，29（5）:202-208.
［13］刘国文，张彩霞，李斌，等. 基于改进RetinaNet模型的接触网鸟巢检测［J］. 数据采集与处理， 2020，35（3）:563-571.
［14］ LI J， YAN D F， LUAN K， et al. Deep learning-based bird's nest detection on transmission lines using UAV imagery［J］. Applied Sciences， 2020，10（18）. DOI: 10.3390/app10186147.
［15］钟映春，孙思语，吕帅，等. 铁塔航拍图像中鸟巢的YOLOv3识别研究［J］. 广东工业大学学报， 2020，37（3）:42-48.
［16］谢国波，郑晓锋，林志毅，等. 基于改进YOLOv4算法的高压塔鸟巢检测［J］. 电子测量技术， 2022，45（18）:145-152.
［17］齐鹏文，李渊，李岩，等. 基于注意力模型的输电线路隐患检测［J］. 电子科技大学学报， 2023，52（2）:240-246.
［18］ ZHU L， WANG X J， KE Z H， et al. BiFormer: Vision transformer with bi-level routing attention［C］// Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE， 2023:10323-10333.
［19］ WOO S， PARK J， LEE J Y， et al. CBAM: Convolutional block attention module［C］// Proceedings of the 2018 European Conference on Computer Vision （ECCV）. Springer， 2018: 3-19.
［20］ CHEN G P， LI L， ZHANG J X， et al. Rethinking the unpretentious U-net for medical ultrasound image segmentation［J］. Pattern Recognition， 2022，142. DOI: 10.1016/j.patcog.2023.109728.
［21］ YU W H， ZHOU P， YAN S C， et al. InceptionNeXt: When Inception meets ConvNeXt［J］. arXiv preprint arXiv:2303.16900， 2023.
［22］ CHEN J R， KAO S H， HE H， et al. Run， don't walk: Chasing higher FLOPS for faster neural networks［C］// Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2023:12021-12031.
［23］ SANDLER M， HOWARD A， ZHU M L， et al. MobileNetV2: Inverted residuals and linear bottlenecks［C］// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. IEEE， 2018:4510-4520.
［24］毕含嘉，杨楚睿，王小雨，等. 基于改进YOLOv7的输电线路多类缺陷目标检测［J/OL］. 电子科技:（2024-04-30）［2024-05-02］1-11. https://link.cnki.net/urlid/61.1291.TN. 20240429.1758.001.

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