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

doi:10.12133/j.smartag.SA202508023

Abstract

Abstract:

Objective Due to the difficulty of detecting dense and small aquaculture net cages in complex backgrounds, this study aims 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 (ADPA) 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 (SPPF) was integrated to effectively reduce background noise interference and improve global feature extraction capabilities. Finally, a mixed attention block (MAB) was incorporated to further enhance the model's perception of dense and small objects. Results and Discussions Experimental results showed that Cage-YOLO achieved improvements over the original YOLOv5 in terms of precision (P), recall (R), and mean average precision (mAP) 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 cages, object detection, adaptive dense perception, 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 on Remote Sensing Images[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202508023.

Figures/Tables 18

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Fig.12

Table 1

Table 2

Table 3

Table 4

Table 5

Fig. 13

References 40

[1]	DRACH A, TSUKROV I, DECEW J, et al. Engineering procedures for design and analysis of submersible fish cages with copper netting for exposed marine environment[J]. Aquacultural engineering, 2016, 70: 1-14.
[2]	WEN L, CHENG Y, FANG Y, et al. A comprehensive survey of oriented object detection in remote sensing images[J]. Expert systems with applications, 2023, 224: ID 119960.
[3]	CHANDRA M A, BEDI S S. Survey on SVM and their application in imageclassification[J]. International journal of information technology, 2021, 13(5): 1-11.
[4]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// 2014 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, New Jersey, USA: IEEE, 2014: 580-587.
[5]	GIRSHICK R. Fast R-CNN[C]// 2015 IEEE International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2015: 1440-1448.
[6]	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149.
[7]	LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]//Computer Vision – ECCV 2016. Cham: Springer, 2016: 21-37.
[8]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2016: 779-788.
[9]	REDMON J, FARHADI A. YOLO9000: Better, faster, stronger[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). J Piscataway, New Jersey, USA: IEEE, 2017: 6517-6525.
[10]	REDMON J, FARHADI A. YOLOv3: An incremental improvement[EB/OL]. arXiv: 1804.02767, 2018.
[11]	BOCHKOVSKIY A, WANG C Y, LIAO H M. YOLOv4: Optimal speed and accuracy of object detection[EB/OL]. arXiv: 2004.10934, 2020.
[12]	GE Z, LIU S T, WANG F, et al. YOLOX: Exceeding YOLO series in 2021[EB/OL]. arXiv: 2107.08430, 2021.
[13]	MIAO L Z, LI N, ZHOU M L, et al. CBAM-Yolov5: Improved Yolov5 based on attention model for infrared ship detection[C]// International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2021). Burlingame, California, USA: SPIE, 2022: 33.
[14]	WANG M, YANG W Z, WANG L J, et al. FE-YOLOv5: Feature enhancement network based on YOLOv5 for small object detection[J]. Journal of visual communication and image representation, 2023, 90: ID 103752.
[15]	KIM M, JEONG J, KIM S. ECAP-YOLO: Efficient channel attention pyramid YOLO for small object detection in aerial image[J]. Remote sensing, 2021, 13(23): ID 4851.
[16]	LUO X D, WU Y Q, WANG F Y. Target detection method of UAV aerial imagery based on improved YOLOv5[J]. Remote sensing, 2022, 14(19): ID 5063.
[17]	SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2015: 1-9.
[18]	TIAN Z, SHEN C H, CHEN H, et al. FCOS: Fully convolutional one-stage object detection[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2020: 9626-9635.
[19]	KONG T, SUN F C, LIU H P, et al. FoveaBox: Beyound anchor-based object detection[J]. IEEE transactions on image processing, 2020, 29: 7389-7398.
[20]	LAW H, DENG J. CornerNet: Detecting objects as paired keypoints[C]// Computer Vision – ECCV 2018. Cham, Germany: Springer, 2018: 765-781.
[21]	LU X, LI B Y, YUE Y X, et al. Grid R-CNN[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2020: 7355-7364.
[22]	YANG Z, LIU S H, HU H, et al. RepPoints: Point set representation for object detection[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Piscataway, New Jersey, USA: IEEE, 2020: 9656-9665.
[23]	CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]// Computer Vision – ECCV 2020. Cham, Germany: Springer, 2020: 213-229.
[24]	ZHU X Z, SU W J, LU L W, et al. Deformable DETR: Deformable transformers for end-to-end object detection[EB/OL]. arXiv: 2010.04159, 2020.
[25]	SUN P Z, ZHANG R F, JIANG Y, et al. Sparse R-CNN: End-to-end object detection with learnable proposals[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2021: 14449-14458.
[26]	BAI Y C, ZHANG Y Q, DING M L, et al. SOD-MTGAN: Small object detection via multi-task generative adversarial network[C]// Computer Vision – ECCV 2018. New York, USA: ACM, 2018: 210-226.
[27]	BAI Y C, ZHANG Y Q, DING M L, et al. Finding tiny faces in the wild with generative adversarial network[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, New Jersey, USA: IEEE, 2018: 21-30.
[28]	LI J N, LIANG X D, WEI Y C, et al. Perceptual generative adversarial networks for small object detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2017: 1951-1959.
[29]	LI C L, YANG T, ZHU S J, et al. Density map guided object detection in aerial images[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Piscataway, New Jersey, USA: IEEE, 2020: 737-746.
[30]	HUANG S Q, LIU Q. Addressing scale imbalance for small object detection with dense detector[J]. Neurocomputing, 2022, 473: 68-78.
[31]	ZHANG X D, IZQUIERDO E, CHANDRAMOULI K. Dense and small object detection in UAV vision based on cascade network[C]// 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). Piscataway, New Jersey, USA: IEEE, 2020: 118-126.
[32]	HE K M, ZHANG X Y, REN S Q, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[C]// Computer Vision – ECCV 2014. Cham, Germany: Springer, 2014: 346-361.
[33]	LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway, New Jersey, USA: IEEE, 2018: 8759-8768.
[34]	LIN T Y, DOLLÁR P, GIRSHICK R, et al. Feature pyramid networks for object detection[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2017: 936-944.
[35]	ZHENG Z H, WANG P, LIU W, et al. Distance-IoU loss: Faster and better learning for bounding box regression[J]. Proceedings of the AAAI conference on artificial intelligence, 2020, 34(7): 12993-13000.
[36]	LIU K, HUANG J, LI X L. Eagle-eye-inspired attention for object detection in remote sensing[J]. Remote sensing, 2022, 14(7): ID 1743.
[37]	FANG Q Y, WANG Z K. Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery[J]. Pattern recognition, 2022, 130: ID 108786.
[38]	WANG Z M, WANG J S, YANG K, et al. Semantic segmentation of high-resolution remote sensing images based on a class feature attention mechanism fused with Deeplabv3+[J]. Computers & geosciences, 2022, 158: ID 104969.
[39]	HOU Q B, ZHOU D Q, FENG J S. Coordinate attention for efficient mobile network design[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2021: 13708-13717.
[40]	WANG Q L, WU B G, ZHU P F, et al. ECA-net: Efficient channel attention for deep convolutional neural networks[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey, USA: IEEE, 2020: 11531-11539.

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
Cage-YOLO	95.5	93.3	95.2	54.9	16.9

Baseline	ADPA	ESSPF	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	ESSPF	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
√	—	√	—	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
Cage-YOLOv12s	87.9	72.3	78.9	43.8