| 摘要: |
| 目的 针对机器人清理河道漂浮物作业中的计算能力有限和河道环境复杂造成的小目标漏检等问题,在
YOLOv7 基础上提出了一种兼顾精度与轻量化的 YOLOv7 目标检测模型。 方法 该模型是在原 YOLOv7 上增加一个
大小为 160×160 的小目标检测层,提升对小目标的特征学习能力,并在原网络模型的颈部引入 BiFormer 注意力机
制,增强对小目标的检测性能,以减少在河道漂浮物检测过程中的漏检现象;其次用 PELAN 替换主干网络的 ELAN
模块,减少算法的参数量和运算量,提高网络检测速度;在此基础上,引入 MPDIoU 损失函数,提升边界框回归的收
敛速度和精度,提高网络模型的鲁棒性。 结果 该改进模型在欧卡智舶的 FloW 子数据集下的 mAP 达到 71. 5%,
相比原 YOLOv7 网络模型提升了 4. 7%,模型参数量和运算量分别降低 11%和 7. 3%。 结论 通过对比实验表明:
整体效果优于原网络模型与传统经典目标检测网络模型,在提高精度的同时,能够减少模型复杂度,更贴合于
实际应用。 |
| 关键词: 小目标检测 YOLOv7 网络模型 BiFormer 轻量化 损失函数 |
| DOI： |
| 分类号: |
| 基金项目: |
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| A Small Target Detection Model for River Floating Objects Based on Improved YOLOv7 |
|
WANG Guoming SONG Jian
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School of Computer Science and Engineering Anhui University of Science and Technology Huainan 232001 Anhui
China
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| Abstract: |
| Objective In the operation of cleaning river floating objects using robots limited computational capacity and
complex river environments often lead to the missed detection of small targets. To address these issues an improved
YOLOv7 object detection model that balances accuracy and lightweight design is proposed based on YOLOv7.
Methods First a small target detection layer with a size of 160×160 was added to the original YOLOv7 to enhance the
feature learning ability for small objects. The BiFormer attention mechanism was introduced into the neck of the original
network model to improve detection performance for small targets thereby reducing missed detections during river floating
object detection. Second the ELAN module in the backbone was replaced with PELAN to reduce the number of
parameters and computational overhead thus increasing the detection speed. On this basis the MPDIoU loss function was
incorporated to accelerate the convergence and improve the accuracy of bounding box regression and enhance the
robustness of the network. Results The improved model achieved a mean average precision mAP of 71. 5% on the
FloW subset from Oka-Zhibo which was 4. 7% higher than that of the original YOLOv7 model. Moreover the number of
parameters and computational cost were reduced by 11% and 7. 3% respectively. Conclusion Comparative experiments
show that the overall performance of the improved model is better than that of the original network model and traditional
classic target-detection network models. The proposed model can reduce the model complexity while improving the accuracy making it more suitable for practical applications. |
| Key words: small target detection YOLOv7 network model BiFormer lightweight loss function area segmentation end-to-end training |
