基于YoloX-ECA模型的非法野泳野钓现场监测技术

doi:10.11996/JG.j.2095-302X.2023030465

摘要/Abstract

摘要：

我国每年有大量人员因为非法在水库及河湖水域游泳、钓鱼溺水而丧生，这些水域往往地处偏僻，无法安排监管人员24小时值守。同时，由于速度和准确度难以兼具，或体积较大难于部署等原因，现有基于视觉的目标检测方法也无法实现对非法野泳、野钓行为的实时检测。基于此，提出了融合注意力机制的YoloX-ECA模型，通过在YoloX骨干网络中的残差块和特征金字塔网络中添加ECA模块，以求在保持原YoloX模型较快检测速度的同时提升对野泳、野钓行为的检测效果。基于自制野泳、野钓数据集的实验证明，改进的YoloX-ECA模型对河湖水域的野泳、野钓行为的检测性能(AP)在90%以上，检测速度为62.29 fps。模型整体性能(mAP)较原YoloX模型提高1.21%，同其他目标检测算法如Faster-RCNN相比性能同样占优。改进的YoloX-ECA模型的实时性和准确性均达到预期设计目标，在河湖流域智能监管等领域有较大的应用前景。

关键词: YoloX, ECA, 目标检测, 注意力机制, 河湖监管

Abstract:

Every year in China, a large number of people die from drowning due to illegal swimming and fishing in reservoirs, rivers, and lakes. These bodies of water are often located in remote areas, making it difficult for staff to supervise them 24 hours a day. Existing target detection methods are either too slow or inaccurate, too large to deploy, or incapable of detecting illegal swimming and fishing in real-time. To address these shortcomings, we proposed the YoloX-ECA model with an attention model. By adding the efficient channel attention (ECA) block to the CSPLayer and FPN, we aimed to improve detection performance for swimming and fishing while maintaining detection speed. Experimental results on self-made datasets showed that the YoloX-ECA achieved over 90% AP for the detection of swimming and fishing classes, with a detection speed of 62.29 fps. Compared with YoloX, mAP was increased by 1.21%. Furthermore, YoloX-ECA’s performance also outperformed other target detection algorithms such as Faster-RCNN. The improved YoloX-ECA model achieved the expected design goals and displayed great prospects for application in the field of intelligent supervision of rivers and lakes.

Key words: YoloX, ECA, object detection, attention model, river and lake supervision

中图分类号:

TP391

罗文宇, 傅明月. 基于YoloX-ECA模型的非法野泳野钓现场监测技术[J]. 图学学报, 2023, 44(3): 465-472.

LUO Wen-yu, FU Ming-yue. On-site monitoring technology of illegal swimming and fishing based on YoloX-ECA[J]. Journal of Graphics, 2023, 44(3): 465-472.

图/表 13

图1 YoloX解耦头与耦合头对比

Fig. 1 YoloX decoupled head compared with coupled head

图2 SEnet网络结构

Fig. 2 SEnet structure

图3 Cbam空间维度网络结构

Fig. 3 Cbam Spatial Attention Module network structure

图4 ECA网络结构

Fig. 4 ECA network structure

图5 融合注意力机制的YoloX网络结构

Fig. 5 YoloX network structure with attention mechanism

表1 实验环境

Table 1 Experimental environment

硬件设备	配置
GPU	RTX 2060
显存	6 GB
软件	版本
Pytorch	1.9
CUDA	10.2
Python	3.8

图6 训练损失曲线

Fig. 6 Training loss curves ((a) YoloX-ECA; (b) YoloX-CBAM; (c) YoloX-SE models)

图7 精准度曲线

Fig. 7 Precision curves ((a) Swimming; (b) Fishing; (c) Warning)

表2 YoloX-ECA，YoloX-CBAM，YoloX-SE和YoloX的精确率、召回率和AP

Table 2 Precisio, Recall and AP of YoloX-ECA, YoloX-CBAM, YoloX-SE, YoloX

检测对象	模型	Precisio (%)	Recall (%)	AP (%)
Swimming	YoloX-ECA	81.29	93.75	94.17
	YoloX-CBAM	77.21	90.29	91.75
	YoloX	81.23	91.69	93.62
	YoloX-SE	77.37	90.78	92.63
Fishing	YoloX-ECA	87.17	95.96	96.70
	YoloX-CBAM	83.92	93.92	93.19
	YoloX	83.04	94.95	93.25
	YoloX-SE	78.85	91.94	89.33
Warning	YoloX-ECA	77.75	79.09	82.80
	YoloX-CBAM	82.63	80.11	85.63
	YoloX	81.82	74.55	83.18
	YoloX-SE	79.11	78.18	83.98

图8 F1-score柱状图

Fig. 8 F1-score histogram ((a) YoloX-ECA; (b) YoloX-CBAM; (c) YoloX-SE; (d) YoloX models)

图9 YoloX-ECA，YoloX-CBAM，YoloX-SE，YoloX和Faster-RCNN的mAP

Fig. 9 mAP of YoloX-ECA, YoloX-CBAM, YoloX-SE, YoloX and Faster-RCNN

图10 YoloX-ECA的检测结果((a)，(b) Swimming类预测；(c) Swimming和Warning类预测；(d)，(e)，(f) Warning类预测；(g)，(h) Fishing类预测)

Fig. 10 Example of YoloX-ECA model detection (((a), (b) Swimming predictions; (c) Swimming and Warning predictions; (d), (e), (f) Warning predictions; (g), (h) Fishing predictions))

图11 检测结果对比图

Fig. 11 Comparison of detection results ((a) YoloX-ECA; (b) Faster-RCNN)

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