Defect detection of aero-engine blades based on dynamic vision sensors

doi:10.11996/JG.j.2095-302X.2026010120

Abstract

Abstract:

Aeroengine blades are core components of engines; tiny surface defects can lead to serious safety accidents. Traditional vision detection technology is limited by motion blur, low dynamic range, background redundancy, and so forth. To address these challenges, a method of aeroengine blade defect detection based on Dynamic Vision Sensor (DVS) was proposed. Dynamic vision sensor produced data in an asynchronous event-stream format, and were therefore referred to as event camera, which exhibited the advantages of large dynamic range, high frame rate, and strong ability to capture small targets. Firstly, a defect detection platform based on DVS was built, and its imaging characteristics and advantages were explored. On this basis, the first Event-based Defect Detection Dataset of Aeroengine Blade (EDD-AB) dataset based on DVS was constructed, covering nearly 6 000 images of scratches, point marks and edge damage, with approximately 12 000 finely annotated target labels. The dataset was released as open source (link: https://github. com/NiBieZhouMei5520/EDD-AB.git). Furthermore, a multi-scale defect-detection algorithm based on asynchronous event-stream frame aggregation (AEAF-ABDD) was proposed: event streams were visualized through frame aggregation technology using a fixed time window; a Multi-Resolution Adaptive Feature Pyramid Network (MRAFPN) was developed to enhance multi-scale defect feature extraction capability; a lightweight SimAM attention mechanism was incorporated to strengthen focus on key regions; a star-convolution module (StarNet) was fused to improve the efficiency of high-dimensional nonlinear feature mapping, enabling accurate detection of multi-scale defects on complex curved workpieces. Experiments demonstrated that AEAF-ABDD achieved a mean Average Precision (mAP) of 97.7% on the EDD-AB dataset and a detection speed of 105 frames per second, substantially outperforming mainstream algorithms. An efficient solution for automated quality inspection of highly reflective curved workpieces was thereby provided, promoting the application of DVS in the field of industrial inspection.

Key words: dynamic vision sensor, aeroengine blades, defect detection, asynchronous event stream, multi-scale feature

CLC Number:

ZHANG Xingshun, CHEN Haiyong. Defect detection of aero-engine blades based on dynamic vision sensors[J]. Journal of Graphics, 2026, 47(1): 120-130.

Figures/Tables 15

Fig. 1 Schematic diagram of the event generation principle

Fig. 2 Schematic diagram of defect imaging ((a) Optical path with a stationary workpiece；(b) The optical path during relative displacement between the workpiece and the DVS)

Fig. 3 Experimental platform

Fig. 4 Comparison of imaging of weak defects (surface scratch defects in red box) ((a) DVS imaging effect; (b) Traditional camera imaging effect)

Fig. 5 High dynamic range imaging comparison (surface scratch defects and point scratch defects in the red box) ((a) DVS imaging effect; (b) Traditional camera imaging effect)

Fig. 6 Visualization images of event flows for different types of defects (the scratches are in the red boxes, the dot marks are in the red circles, and the edge damages are in the red triangles)

Fig. 7 EDD-AB Characteristics Statistics of Dataset ((a) Area distribution of bounding boxes for seratches; (b) Area distribution of bounding boxes for point marks; (c) Area distribution of bounding boxes for edge damage; (d) Area distribution of bounding boxes for all defect targets)

Fig. 8 Small target detection head

Fig. 9 Schematic diagram of the SimAM attention structure

Fig. 10 Schematic diagram of star-shaped convolution operation(* is element-wise multiplication)

Table 1 Experimental environment configuration

配置	参数
操作环境	Windows11
深度学习框架	Pytorch1.12
CUDA	11.3
Python版本	Python-3.12
CPU	Intel(R) Core(TM) i7-14700HX 2.10 GHz
GPU	NVIDIA GeForce RTX 4070Laptop 8 GB

Table 2 Experimental parameter settings

超参数	参数值	超参数	参数值
Images size	640×640	Optimize	SGD
Epochs	300	Momentum	0.973
Batch size	32	Learning rate	0.01

Table 3 Comparative experiments with other algorithms on the EDD-AB dataset

Method	P/%	R/%	Parms/10⁶	GFLOPs	FPS	mAP/%
Method	P/%	R/%	Parms/10⁶	GFLOPs	FPS	综合	划痕	点痕	边缘损伤
Faster-RCNN	76.6	77.6	165.00	199.0	66	80.2	87.7	66.2	86.6
SSD	63.0	65.3	24.50	87.9	72	71.0	77.6	55.3	79.6
DETR	77.5	78.0	9.49	16.8	88	87.0	91.3	77.5	91.8
Yolov10	89.6	87.5	2.59	6.4	96	92.8	97.1	85.7	96.0
Yolov12	85.3	88.7	2.52	6.0	93	91.8	94.8	89.1	92.2
EMS-YOLO^[18]	82.3	84.5	14.40	6.8	90	88.8	90.2	83.3	91.9
TIFF-EDD^[13]	90.6	91.1	3.06	28.4	82	94.1	95.8	89.9	96.5
AEAF-ABDD	93.2	93.9	3.00	12.8	105	97.7	98.6	95.9	97.9

Fig. 11 Comparison of hatemap detection ((a) Original image; (b) DETR; (c) Yolov10; (d) TIFF-EDD; (e) AEAF-ABDD)

Table 4 Ablation experiments on the EDD-AB dataset

Base	MSAFPN	SimAM	StarNet	P/%	R/%	Parms/10⁶	GFLOPs	FPS	mAP/%
Base	MSAFPN	SimAM	StarNet	P/%	R/%	Parms/10⁶	GFLOPs	FPS	综合	划痕	点痕	边缘损伤
√				87.1	89.6	3.00	8.2	99	92.9	96.4	87.8	94.7
√	√			92.6	93.0	2.93	12.4	100	94.0	96.4	87.6	96.5
√		√		89.6	90.8	3.01	8.2	98	93.6	95.4	90.9	97.0
√			√	88.6	90.5	3.08	8.5	89	94.1	96.9	89.2	95.8
√	√	√		91.9	92.3	2.93	12.4	101	95.5	97.6	93.2	96.5
√		√	√	90.3	90.8	3.08	8.5	96	94.2	96.3	88.6	96.6
√	√		√	91.6	92.5	3.00	12.8	93	95.2	95.8	89.7	97.3
√	√	√	√	93.2	93.9	3.00	12.8	105	97.7	98.6	95.9	97.9

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