Pavement defect detection algorithm with enhanced morphological perception

doi:10.11772/j.issn.1001-9081.2024101511

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (10): 3342-3352.DOI: 10.11772/j.issn.1001-9081.2024101511

• Frontier and comprehensive applications • Previous Articles

Pavement defect detection algorithm with enhanced morphological perception

Jiahui ZHANG, Xiaoming LI(), Jiaxiang ZHANG

College of Computer Science and Technology，Taiyuan University of Science and Technology，Taiyuan Shanxi 030024，China

Received:2024-10-24 Revised:2025-01-07 Accepted:2025-01-10 Online:2025-01-15 Published:2025-10-10
Contact: Xiaoming LI
About author:ZHANG Jiahui， born in 1999， M. S. candidate. Her research interests include computer vision， object detection.
LI Xiaoming， born in 1965， Ph. D.， professor. His research interests include computer vision， image processing and analysis.
ZHANG Jiaxiang， born in 1999， M. S. candidate. His research interests include computer vision， object detection.
Supported by:
National Natural Science Foundation of China(62273248)

强化形态感知的路面缺陷检测算法

张佳慧, 李晓明(), 张嘉祥

太原科技大学计算机科学与技术学院，太原 030024

通讯作者: 李晓明
作者简介:张佳慧（1999—），女，山西榆社人，硕士研究生，CCF会员，主要研究方向：计算机视觉、目标检测
李晓明（1965—），男，山西太原人，教授，博士，CCF会员，主要研究方向：计算机视觉、图像处理与分析 Email:lixiaoming@tyust.edu.cn
张嘉祥（1999—），男，山西文水人，硕士研究生，CCF会员，主要研究方向：计算机视觉、目标检测。
基金资助:
国家自然科学基金资助项目(62273248)

Abstract

Abstract:

To address the problems of low detection accuracy and high missed detection rate caused by the narrow lateral， multi-scale， and long-range dependency characteristics of pavement defect morphology， a pavement defect detection algorithm improved by YOLOv8_n with enhanced morphological perception was proposed. Firstly， an Edge-Enhancement Focus Module （EEFM） was introduced in the backbone fusion stage， a strip pooling kernel was used to capture directional and position-aware information， thereby enhancing edge details in deep features and improving representation ability of elongated features. Secondly， a Dual Chain Feature Redistribution Pyramid Network （DCFRPN） was designed to reconstruct the fusion method， so as to provide multi-scale features with extensive perception and rich localization information， thereby improving fusion ability for multi-scale defects. Additionally， a Morphological Aware Task Interaction Detection Head （MATIDH） was constructed to enhance task interaction between classification and localization， thereby adjusting data representation dynamically and integrating multi-scale strip convolutions to optimize the classification and regression of elongated defects. Finally， a PWIoU （Penalized Weighted Intersection over Union） loss function was proposed to allocate gradient gains dynamically for prediction boxes of different qualities， thereby optimizing the regression of bounding boxes. Experimental results show that on the RDD2022 dataset， compared to YOLOv8_n， the proposed algorithm has the precision and recall improved by 3.5 and 2.3 percentage points， respectively， and the mean Average Precision （mAP） at 50% Intersection over Union （IoU） increased by 3.2 percentage points， verifying the effectiveness of the proposed algorithm.

Key words: YOLOv8_n, pavement defect detection, long-range dependency, crack, multi-scale

摘要：

针对路面缺陷形态侧向狭窄、尺度多变和长程依赖特性导致检测精度低和漏检率高的问题，提出基于YOLOv8_n改进的强化形态感知的路面缺陷检测算法。首先，在主干网络融合阶段提出边缘增益聚焦模块（EEFM），采用条状池化核捕捉定向和位置感知信息并强化深层特征的边缘细节，增强细长特征的表达能力。其次，设计双链特征重分配金字塔网络（DCFRPN），重构融合方式，提供大范围感知和丰富定位信息的多尺度特征，提升对多尺度缺陷的融合能力。此外，构造形态感知任务交互检测头（MATIDH），增强分类与定位之间的任务交互，动态调整数据表征，融合多尺度带状卷积，优化细长缺陷的分类和回归。最后，提出PWIoU（Penalized Weighted Intersection over Union）损失函数，动态分配不同质量预测框的梯度增益，优化Box框的回归方式。实验结果表明，在RDD2022数据集上，所提算法的精确率和召回率相较于YOLOv8_n分别提升了3.5和2.3个百分点，在50%交并比（IoU）阈值下的平均精度均值（mAP）提升了3.2个百分点，验证了所提算法的有效性。

关键词: YOLOv8_n, 路面缺陷检测, 长程依赖关系, 裂缝, 多尺度

CLC Number:

TP391.41

Jiahui ZHANG, Xiaoming LI, Jiaxiang ZHANG. Pavement defect detection algorithm with enhanced morphological perception[J]. Journal of Computer Applications, 2025, 45(10): 3342-3352.

张佳慧, 李晓明, 张嘉祥. 强化形态感知的路面缺陷检测算法[J]. 《计算机应用》唯一官方网站, 2025, 45(10): 3342-3352.

Figures/Tables 19

References 41

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注意力机制	AP₅₀ /%		mAP₅₀/%	mAP_50：95/%	Params/10⁶
注意力机制	D00	D10	mAP₅₀/%	mAP_50：95/%	Params/10⁶
Baseline	59.2	59.9	58.9	30.3	3.01
GAM	60.3	58.5	58.1	30.3	5.28
LSKA	60.0	59.4	59.4	31.0	3.71
DA	56.8	57.1	57.5	28.3	3.91
ConvFFN	56.9	56.9	56.7	28.3	3.95
AA	57.4	57.4	57.7	28.6	3.98
MPCA	60.4	60.6	59.8	31.0	3.70

注意力机制	AP₅₀ /%		mAP₅₀/%	mAP_50：95/%	Params/10⁶
注意力机制	D00	D10	mAP₅₀/%	mAP_50：95/%	Params/10⁶
Baseline	59.2	59.9	58.9	30.3	3.01
GAM	60.3	58.5	58.1	30.3	5.28
LSKA	60.0	59.4	59.4	31.0	3.71
DA	56.8	57.1	57.5	28.3	3.91
ConvFFN	56.9	56.9	56.7	28.3	3.95
AA	57.4	57.4	57.7	28.6	3.98
MPCA	60.4	60.6	59.8	31.0	3.70

注意力机制	AP₅₀/%		mAP₅₀/%	mAP_50：95/%	Params/10⁶
注意力机制	D00	D10	mAP₅₀/%	mAP_50：95/%	Params/10⁶
Baseline	59.2	59.9	58.9	30.3	3.01
LSKA	60.7	60.5	59.8	30.9	2.37
ConvFFN	56.2	56.8	56.9	28.2	2.39
MSCA	60.9	60.7	60.1	31.1	2.38

注意力机制	AP₅₀/%		mAP₅₀/%	mAP_50：95/%	Params/10⁶
注意力机制	D00	D10	mAP₅₀/%	mAP_50：95/%	Params/10⁶
Baseline	59.2	59.9	58.9	30.3	3.01
LSKA	60.7	60.5	59.8	30.9	2.37
ConvFFN	56.2	56.8	56.9	28.2	2.39
MSCA	60.9	60.7	60.1	31.1	2.38

IoU	AP₅₀				mAP₅₀	mAP_50：95
IoU	D00	D10	D20	D40	mAP₅₀	mAP_50：95
CIoU	61.5	62.0	69.0	50.7	60.8	31.9
WIoUv3	61.0	61.3	69.1	52.6	61.0	31.9
WIoUv2	60.9	60.3	69.4	53.3	61.0	31.9
WIoUv1	61.0	61.7	68.6	52.4	60.9	32.1
PIoUv1	61.1	61.2	68.2	51.2	60.4	32.0
PIoUv2	61.1	60.8	67.9	52.4	60.6	31.7
PWIoU	62.3	62.5	69.7	53.7	62.1	32.1

Pavement defect detection algorithm with enhanced morphological perception

强化形态感知的路面缺陷检测算法

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Figures/Tables 19

References 41

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Metrics

EEFM	PWIoU	MATIDH	DCFRPN	AP₅₀/%				P/%	R/%	mAP₅₀/%	计算量/GFLOPS	Params/10⁶
EEFM	PWIoU	MATIDH	DCFRPN	D00	D10	D20	D40	P/%	R/%	mAP₅₀/%	计算量/GFLOPS	Params/10⁶
				59.2	59.9	67.9	48.6	63.8	54.4	58.9	8.2	3.01
√				60.4	60.6	67.8	50.2	65.0	54.4	59.8	8.8	3.70
	√			60.3	60.4	67.9	51.5	64.6	54.9	60.0	8.2	3.01
		√		60.9	60.7	68.1	50.7	64.8	54.4	60.1	8.0	2.45
			√	60.4	60.8	68.7	52.0	65.1	55.1	60.5	9.5	3.64
√	√			61.0	60.7	68.9	51.4	65.1	54.9	60.5	8.8	3.64
√	√	√		61.7	61.8	69.1	52.6	67.2	56.4	61.3	7.6	3.02
√	√	√	√	62.3	62.5	69.7	53.7	67.3	56.7	62.1	9.1	3.93

算法	AP₅₀/%				mAP₅₀/%	mAP_50：95/%	P/%	R/%	计算量/GFLOPS	Params/10⁶
算法	D00	D10	D20	D40	mAP₅₀/%	mAP_50：95/%	P/%	R/%	计算量/GFLOPS	Params/10⁶
Faster R-CNN^［35］	61.9	60.1	63.8	52.4	59.5	27.5	52.3	52.9	121.45	60.13
FCOS	60.0	52.2	65.0	51.5	57.2	25.9	58.9	42.5	109.12	50.78
VFNet^［36］	61.8	58.6	64.1	50.2	58.7	28.6	48.0	52.4	106.04	51.48
TOOD	61.5	59.2	64.6	50.5	58.9	28.4	55.9	48.5	153.00	53.81
YOLOv5_s	60.9	59.9	68.4	51.8	60.2	30.9	65.7	55.3	24.00	9.12
YOLOv5_n	58.6	57.9	66.3	46.9	57.4	29.0	66.2	50.9	7.10	2.50
YOLOX_s	61.8	60.3	69.1	53.1	61.1	29.8	63.9	56.5	13.30	8.94
YOLOv7_t^［37］	60.7	56.7	65.4	52.2	58.8	27.7	63.6	54.5	13.20	6.02
YOLOv8_n	59.2	59.9	67.9	48.6	58.9	30.3	63.8	54.4	8.20	3.01
Gold-YOLO^［38］	60.2	59.9	67.8	51.8	59.9	29.7	63.5	56.4	12.05	5.61
RT-DETR-l^［39］	60.5	60.2	61.3	54.7	59.2	29.0	63.5	55.2	105.20	29.30
Swin-T^［40］	57.8	47.5	67.7	49.5	55.6	25.0	48.0	49.0	83.94	36.88
YOLOv9_n	59.9	59.2	65.7	44.4	57.3	29.1	64.1	52.3	10.70	2.62
YOLOv10_n^［41］	58.0	56.6	66.4	45.0	56.5	29.1	61.5	52.7	8.20	2.70
YOLOv10_s	60.1	58.0	67.8	50.4	59.1	30.8	65.9	53.0	24.50	8.04
YOLOv11_n	53.2	54.5	63.6	40.4	53.0	26.2	61.0	49.5	6.30	2.58
YOLOv11_s	56.6	57.5	65.3	47.9	56.8	28.2	62.4	54.2	21.30	9.41
本文算法	62.3	62.5	69.7	53.7	62.1	32.1	67.3	56.7	9.10	3.93

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算法	AP₅₀/%						P/%	R/%	mAP₅₀/%	mAP_50：95/%
算法	LC	TC	AC	PH	OC	RP	P/%	R/%	mAP₅₀/%	mAP_50：95/%
Baseline	80.5	82.3	89.2	46.6	75.7	91.7	81.2	73.5	77.7	46.8
本文算法	86.8	85.3	93.3	57.5	82.2	92.1	88.2	74.8	82.9	52.7

算法	AP₅₀/%						P/%	R/%	mAP₅₀/%	mAP_50：95/%
算法	LC	TC	AC	PH	OC	RP	P/%	R/%	mAP₅₀/%	mAP_50：95/%
Baseline	80.5	82.3	89.2	46.6	75.7	91.7	81.2	73.5	77.7	46.8
本文算法	86.8	85.3	93.3	57.5	82.2	92.1	88.2	74.8	82.9	52.7