基于RT-DETR的浅海底栖生物目标检测改进模型

doi:10.11978/2025098

Abstract

Abstract:

Epibenthic organism target detection is of great significance in marine ecological monitoring and resource management. However, due to the low illumination, blurriness and complex background of underwater images, the performance of traditional detection algorithms is limited. This paper proposes the MEIE-RTDETR (multiscale edge information enhance-real time detection transformer) model. By designing a multi-scale edge information enhancement module to strengthen feature extraction, adopting an adaptive sparse self-attention (ASSA) mechanism to reduce computational redundancy, and introducing an intensity enhance layer (IEL) module to improve the feature pyramid for better small target detection, the model enhances its overall capability. Additionally, a powerful-IoU (P-IoU) + normalized Wasserstein distance (NWD) loss function is designed to improve performance in scenarios with blurred bounding boxes and multi-scale target detection. Experiments on the DUO (detecting underwater objects) and RUOD (rethinking general underwater object detection) datasets show that the improved model outperforms Faster R-CNN (region-based convolutional neural networks), the YOLO (you only look once) series, and the original RT-DETR (real time-detection transformer). It achieves mAP50 scores of 85.0% and 85.4% on the respective datasets with significantly reduced number of parameters and computational complexity, providing an effective solution for lightweight and high-precision epibenthic target detection.

Key words: epibenthos, target detection, RT-DETR, multi-scale feature enhancement, adaptive sparse self-attention (ASSA), lightweight model

CLC Number:

TP391.41

DENG Jianzhi, TANG Zhenghao, LI Yun. An improved model for epibenthic organism target detection based on RT-DETR[J].Journal of Tropical Oceanography, 2026, 45(3): 95-108.

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α	P-IoU: NWD	精准率/%	召回率/%	mAP50/%
0	0∶1	83.1	74.9	83.0
0.2	0.2∶ 0.8	83.3	75.5	83.1
0.4	0.4∶ 0.6	83.4	75.6	83.4
0.5	0.5∶ 0.5	83.9	76.0	83.7
0.6	0.6∶ 0.4	83.3	75.3	83.4
0.8	0.8∶ 0.2	83.1	75.2	83.0
1	1∶ 0	82.9	74.7	82.7

编号	A模块	B模块	C模块	D模块	参数量/MB	计算量/GFLOPs	精准率/%	召回率/%	mAP50/%	mAP50-95/%
1	—	—	—	—	19.87	57.0	83.3	75.3	83.4	67.5
2	√	—	—	—	14.46	49.8	82.7	78.3	84.8	68.9
3	—	√	—	—	20.71	57.8	83.2	78.5	84.7	67.7
4	—	—	√	—	19.81	50.7	80.7	79.2	84.1	67.9
5	—	—	—	√	19.87	56.5	83.9	76.1	83.7	67.7
6	√	√	—	—	15.30	50.0	84.8	77.6	84.8	69.5
7	√	—	√	—	14.40	46.2	84.7	77.9	84.7	69.7
8	√	—	—	√	14.46	49.4	82.9	78.5	84.8	69.1
9	—	√	√	—	20.65	45.0	86.1	78.7	84.9	68.2
10	—	√	—	√	20.71	57.5	83.4	78.7	84.8	68.4
11	—	—	√	√	19.81	50.1	80.8	79.4	84.2	68.2
12	√	√	√	—	15.83	49.8	84.2	77.1	84.7	70.9
13	√	√	—	√	15.30	49.8	84.8	77.8	84.8	70.3
14	√	—	√	√	14.40	46.0	84.8	77.7	84.7	70.2
15	—	√	√	√	20.65	44.9	83.9	78.5	84.6	70.0
16	√	√	√	√	15.83	49.5	84.5	77.7	85.0	71.5

模型名称	参数量/MB	计算量/GFLOPs	DUO			RUOD
模型名称	参数量/MB	计算量/GFLOPs	精准率/%	召回率/%	mAP50/%	精准率/%	召回率/%	mAP50/%
Faster-RCNN	42.22	180.1	78.6	70.5	79.9	78.8	71.2	80.2
YOLOv5	25.04	64.0	81.5	76.1	84.5	84.5	76.8	85.1
YOLOv6	51.84	161.1	84.2	76.6	83.7	82.4	75.4	84.3
YOLOv8	16.48	64.0	83.3	77.0	84.3	84.0	79.8	85.0
YOLOv10	20.05	68.2	80.6	77.5	84.5	84.3	77.1	84.8
DETR	41.00	86.2	80.9	76.3	79.7	81.0	76.8	80.7
Deformable-DETR	20.50	73.6	82.1	74.6	82.4	82.6	75.2	82.9
RT-DETR	19.87	57.0	83.3	75.3	83.4	84.3	76.6	84.0
This study	15.83	49.5	84.5	77.7	85.0	84.9	78.5	85.4

模型	小目标mAP50/%	中目标mAP50/%	大目标mAP50/%
Faster-RCNN	62.1	78.2	83.2
YOLOv5	72.5	82.8	88.5
YOLOv6	72.0	81.4	88.3
YOLOv8	72.5	83.0	89.0
YOLOv10	74.3	82.1	88.7
DETR	71.3	80.9	84.7
Deformable-DETR	72.1	82.4	83.8
RT-DETR	74.8	83.5	89.0
改进后的MEIE-RTDETR	77.2	84.9	89.4

模型名称	RUIE			URPC2019
模型名称	精准率/%	召回率/%	mAP50/%	精准率/%	召回率/%	mAP50/%
Faster R-CNN	75.2	73.3	77.8	75.8	70.3	78.1
YOLOv5	81.6	76.3	84.7	84.5	76.3	85.2
YOLOv6	84.0	76.1	83.5	81.7	75.0	84.0
YOLOv8	83.4	77.3	84.5	84.1	79.8	85.1
YOLOv10	80.4	77.22	84.3	84.1	76.9	84.7
DETR	80.7	76.4	79.6	81.1	76.6	80.5
Deformable-DETR	82.3	74.4	82.6	82.5	75.0	83.0
RT-DETR	83.4	75.7	83.6	84.2	76.2	83.9
改进后的MEIE-RTDETR	84.2	77.7	84.9	84.8	78.1	85.0

An improved model for epibenthic organism target detection based on RT-DETR

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