面向遥感图像舰船识别的轻量化目标检测算法

doi:10.13229/j.cnki.jdxbgxb.20240753

Abstract

Abstract:

To solve the problem of existing deep learning-based object detection algorithms being unsuitable for onboard deployment due to their complexity， a multi-scale feature enhanced lightweight detection algorithm （MFLDet） for ship recognition tasks in optical remote sensing images is proposed. Firstly， to reduce the algorithm's parameter and computation load， a lightweight network architecture， PG-HGNet， is constructed as the backbone network， and constructs a lightweight cross-scale feature fusion network for interactive feature integration. Secondly， a multi-scale feature enhancement module （MFEM） is designed to accommodate the scale variability of ship targets in remote sensing images， thereby enhancing detection accuracy. Finally， the MPDIoU bounding box loss function is introduced to adjust for cases where the predicted and actual bounding boxes share the same aspect ratio but differ in absolute dimensions. Comparative experiments conducted on the HRSC2016 dataset demonstrate that， compared to the baseline model， MFLDet achieves a 55% reduction in parameters and a 23.5% reduction in computation， with only a 0.2 percentage points decrease in average precision， thus effectively balancing complexity and accuracy. Overall， the proposed method surpasses other comparative methods in terms of both lightweight level and detection precision.

Key words: computer vision, object detection, remote sensing image, feature enhancement

CLC Number:

TP391.4

Fan ZHANG,Jing-bo WANG,Yang ZHU,Hai-ying LIU,Yu ZHENG,Wen-hua WANG. Lightweight object detection algorithm for ship recognition in remote sensing image[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(2): 533-542.

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实验编号	mAP_0.5/%	mAP_0.5：0.95/%	参数量/M	FLOPs/G
1	89.8	77	1.50	6.0
2	89.7	75.2	1.76	7.3
3	90.2	76.5	1.97	7.4
4	90.9	78.8	1.35	6.2

模型	mAP_0.5/ %	mAP_0.5：0.95/ %	参数量/ M	FLOPs/ G
基线	91.1	78.7	3.01	8.1
+PG-HGNet	89.8	76.1	2.31	6.8
+LW-BiFPN	91.7	79.1	1.99	7.1
+MFEM	92.2	80.7	3.33	8.7
+MPDIoU	91.9	79.8	3.01	8.1
+PG-HGNet+ LW-BiFPN	90.3	77.2	1.29	5.8
+MFEM+ MPDIoU	92.5	81.1	3.33	8.7
+all	90.9	78.8	1.35	6.2

算法	mAP_0.5/ %	mAP_0.5：0.95/ %	参数量/ M	FLOPs/ G
Faster R-CNN	90.2	65.4	136.69	369.7
SSD	64.5	37.7	23.61	60.8
Yolov3-tiny	90.9	74.6	12.13	18.9
Yolov5nano	90.8	77.3	2.50	7.1
Yolov7-tiny	90.6	71.2	6.23	13.9
Yolov10nano	87.1	74.2	2.69	8.2
本文	90.9	78.8	1.35	6.2

算法	mAP_0.5/%	参数量/M	FLOPs/G
SOCDet^［17］	94.28	10.01	16.72
AOPG^［18］	90.58	33.87	164.33
PP-PicoDet-S^［19］	90.89	1.08	13.92
LO-Det^［20］	90.17	6.78	10.98
VOD-YOLOv7^［21］	92.1	7.45	116
本文	90.9	1.35	6.2

算法	mAP_0.5/ %	mAP_0.5：0.95/ %	参数量/ M	FLOPs/ G
Yolov3-tiny	91.3	56.8	12.1	18.9
Yolov5nano	97.1	61	2.5	7.1
Yolov7-tiny	96.4	61.3	6.23	13.9
Yolov8nano	96.8	61.7	3	8.1
Yolov10nano	96.5	61.1	2.69	8.2
本文	96.6	60.9	1.35	6.2

Lightweight object detection algorithm for ship recognition in remote sensing image

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