A Marine Object Detection Algorithm Based on SSD and Feature Enhancement

• Published in: Complexity (New York, N.Y.) Vol. 2020; no. 2020; pp. 1 - 14
• Main Authors: Deng, Zhiliang, Lu, Meixia, Lu, Feiyu, Hu, Kai, Liu, Yunping
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Hindawi Limited; Wiley
• Subjects: Accuracy; Algorithms; Classification; Deep learning; Detectors; Edge detection; Image enhancement; Machine learning; Neural networks; Object recognition; Robots; Sea urchins; Support vector machines; Target detection; Training; Underwater robots
• ISSN: 1076-2787; 1099-0526
• DOI: 10.1155/2020/5476142

Autonomous detection and fishing by underwater robots will be the main way to obtain aquatic products in the future; sea urchins are the main research object of aquatic product detection. When the classical Single-Shot MultiBox Detector (SSD) algorithm is applied to the detection of sea urchins, it also has disadvantages of being inaccurate to small targets and insensitive to the direction of the sea urchin. Based on the classic SSD algorithm, this paper proposes a feature-enhanced sea urchin detection algorithm. Firstly, according to the spiny-edge characteristics of a sea urchin, a multidirectional edge detection algorithm is proposed to enhance the feature, which is taken as the 4th channel of image and the original 3 channels of underwater image together as the input for the further deep learning. Then, in order to improve the shortcomings of SSD algorithm’s poor ability to detect small targets, resnet 50 is used as the basic framework of the network, and the idea of feature cross-level fusion is adopted to improve the feature expression ability and strengthen semantic information. The open data set provided by the National Natural Science Foundation of China underwater Robot Competition will be used as the test set and training set. Under the same training and test conditions, the AP value of the algorithm in this paper reaches 81.0%, 7.6% higher than the classic SSD algorithm, and the confidence of small target analysis is also improved. Experimental results show that the algorithm in this paper can effectively improve the accuracy of sea urchin detection.