A multi-scale features-based method to detect Oplegnathus

Information Processing in Agriculture

Zhenbo Li¹  Qing Wang²  Guangjie Kou³  Huihui Yang⁴  Jun Yue⁴  Shixiang Jia⁴  Ruijia Ba⁴ 

[1] Corresponding author.;School of Civil Engineering, Ludong University, Yantai 264025, China;School of Information and Electrical Engineering, China Agricultrual University, Beijing 100083, China;School of Information and Electrical Engineering, Ludong University, Yantai 264025, China;

关键词: Detection of the Oplegnathus;    Deep learning;    MobileNet-SSD;    Neural networks;   

DOI  :  

来源: DOAJ

【 摘 要 】

It is of great significance to use underwater video and image processing technology to detect and analyze fish behaviors. In this paper, an Oplegnathus image dataset for fish behaviors study by deep learning algorithm is constructed, and the data is captured from two cameras (one above water and another below water); and then an improved Neural Network model based on multi-scale features is proposed for fish behaviors learning automatically. To overcome the occlusion and blur problems of the images, the lightweight neural network MobileNet-SSD is improved by adding a dilate convolution, and SE blocks are added to the feature maps at different scales to establish a self-attention mechanism; the Focal Loss function is used to calculate the classification loss and to balance the proportion of background and target samples. The results of the experiments show that the average behaviors detection accuracy of our method reach 90.94% and 88.36% in both overwater and underwater datasets.

【 授权许可】

Unknown