CAGE-YOLO: A Dense Small Object Detection Model for Aquaculture Net Cages Based on Remote Sensing Images

doi:10.12133/j.smartag.SA202508023

Abstract

Abstract:

[Objective] Detecting dense and small aquaculture net cages in complex backgrounds is difficult, the purpose of this study is to build a specialized dataset and design a targeted detection model that enhances recognition accuracy and robustness for practical aquaculture management. [Methods] A dataset of aquaculture net cages was constructed using high-resolution remote sensing imagery collected from seven representative farming regions (Australia, Canada, Chile, Croatia, Greece, China, and the Faroe Islands), and Cage-YOLO, a deep learning model based on YOLOv5, was proposed for detecting dense and small aquaculture net cages. First, an adaptive dense perception algorithm was introduced, which automatically selects and generates feature maps that reflect the high-density distribution of small aquaculture net cages. Second, an enhanced module based on spatial pyramid pooling fast was integrated to effectively reduce background noise interference and improve global feature extraction capabilities. Finally, a mixed attention block was incorporated to further enhance the model's perception of dense and small objects. [Results and Discussions] Experimental results showed that the proposed Cage-YOLO achieved improvements over the original YOLOv5 in terms of precision, recall, and mean average precision by 5.6, 21.8, and 17.4 percentage points, respectively. The model size was maintained at 16.9 MB, demonstrating both strong performance and deployment advantages. [Conclusions] This study provides a new approach for dense and small object detection and offers technical support for the intelligent management of marine cage aquaculture.

Key words: aquaculture net cage, small object detection, adaptive dense perception algorithm, enhancement spatial pyramid pooling, mix attention block

CLC Number:

S967.3

ZHANG Wenbo, JIANG Yijue, SONG Wei, HE Qi, ZHANG Wenbo. CAGE-YOLO: A Dense Small Object Detection Model for Aquaculture Net Cages Based on Remote Sensing Images[J]. Smart Agriculture, 2026, 8(2): 98-117.

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References 40

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Method	P/%	R/%	mAP@0.5/%	mAP@0.5:0.95/%	Model size/MB
YOLOv5	89.9	71.5	77.8	41.5	14.4
YOLOv6s	84.8	68.7	71.5	35.3	38.1
YOLOv7	90.8	81.3	87.9	47.4	72.0
YOLOv8s	91.5	77.7	84.7	47.6	22.5
YOLOv9s	84.8	73.7	77.7	41.0	26.7
YOLOv10s	88.6	77.8	82.7	45.9	28.8
YOLOv11s	90.0	78.1	84.4	48.2	37.6
YOLOv12s	87.7	79.4	83.2	47.2	19.1
Faster R-CNN	69.3	77.0	69.3	35.5	108.0
DERT	89.3	77.1	83.2	45.3	158.0
Cage-YOLO	95.5	93.3	95.2	54.9	16.9

Baseline	ADPA	ESPPF	MAB	P/%	R/%	mAP@0.5/%	mAP@0.5:0.95/%
√	—	—	—	89.9	71.5	77.8	41.5
√	√	—	—	90.6	83.9	87.1	48.2
√	—	√	—	95.3	88.2	92.9	54.9
√	—	—	√	89.6	72.8	78.2	40.6
√	√	—	√	94.4	90.5	93.2	54.6
√	—	√	√	95.9	88.7	91.9	54.1
√	√	√	—	93.9	85.3	90.7	51.0
√	√	√	√	95.5	93.3	95.2	54.9

Baseline	ADPA	ESPPF	MAB	circle mAP@0.5/%	rectangular mAP@0.5/%	circle mAP@0.5:0.95/%	rectangular mAP@0.5:0.95/%
√	—	—	—	76.4	95.4	40.8	53.2
√	√	—	—	83.1	96.2	45.1	54.0
√	—	√	—	92.2	96.3	55.0	52.6
√	—	—	√	77.2	95.4	40.6	52.2
√	√	—	√	90.7	95.3	51.5	53.6
√	—	√	√	90.8	96.4	54.8	52.1
√	√	√	—	90.6	93.7	53.4	50.2
√	√	√	√	93.1	95.5	55.0	53.4

YOLOv5+ESPPF+ADPA	CA	ECA	CBAM	MAB	P/%	R/%	mAP@0.5/%	mAP@0.5:0.95/%
√	√	—	—	—	96.1	88.0	91.5	53.0
√	—	√	—	—	96.4	88.6	92.0	53.9
√	—	—	√	—	95.5	89.6	92.4	54.0
√	—	—	—	√	95.5	93.3	95.2	54.9

Method	P/%	R/%	mAP@0.5/%	mAP@0.5:0.95/%
Cage-YOLO	95.5	93.3	95.2	54.9
Cage-YOLOv8s	87.8	78.5	82.7	46.7
Cage-YOLOv10s	87.8	77.4	81.1	46.5
Cage-YOLOv11s	89.5	77.9	83.9	47.0
Cage-YOLOv12s	87.9	72.3	78.9	43.8