Journal
AQUACULTURAL ENGINEERING
Volume 91, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.aquaeng.2020.102117
Keywords
Deep learning; MobileNet; Object detection; Receptive field; YOLO
Categories
Funding
- Youth Science and Technology Talents Project of Liaoning Provincial Department of Education [QL201912]
- Liaoning Natural Science Foundation Project [20180550573]
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This paper proposes a new approach combining YOLOv3 with MobileNetv1 for fish detection in real breeding farm. The feature maps of MobileNet are reselected as per their receptive fields for better fish detection instead of fixed chosen strategy in the original YOLOv3 framework. A set of fish image data acquired in breeding farm is used to evaluate the proposed method. The high accuracy of detection results is achieved to confirm the ef-fectiveness of the proposed method. Furthermore, taking the place of ImageNet, a slighter dataset including fish images with 16 species for backbone network pretraining is picked out from ImageNet to extract fish features. On this basis, the effect of detection of the model is further improved due to that the extracted features are more closed to fish objects. Therefore, the proposed method is proved to have the capability of providing necessary and accurate number of fish, which will then be used to determine the breeding actions accordingly.
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