Automated Soft Pressure Sensor Array-Based Sea Lamprey Detection Using Machine Learning

Sea lamprey, a destructive invasive species in the Great Lakes in North America, is among very few fishes that rely on oral suction during migration and spawning. Recently, soft pressure sensors have been proposed to detect the attachment of sea lamprey as part of the monitoring and control effort. However, human decision is still required for the recognition of patterns in the measured signals. In this article, a novel automated soft pressure sensor array-based sea lamprey detection framework is proposed using object detection convolutional neural networks. First, the resistance measurements of the pressure sensor array are converted to mappings of relative change in resistance. These mappings typically show two different types of patterns under lamprey attachment: a high-pressure circular pattern corresponding to the mouth rim compressed against the sensor (“compression” pattern), and a low-pressure blob corresponding to the partial vacuum region of the sucking mouth (“suction” pattern). Three types of object detection algorithms, single-shot detector (SSD), RetinaNet, and YOLOv5s, are applied to the dataset of measurements collected in the presence of sea lamprey attachment, and the comparison of their performance shows that YOLOv5s model achieves the highest mean average precision (mAP) and the fastest inference speed. Furthermore, to improve the accuracy of the prediction model and reduce the false positive (FP) rate due to the sensor’s memory effect, a filter branch with different detection thresholds for the compression and suction patterns, respectively, is added to the original machine-learning algorithm. The trained model is validated and used to automatically detect sea lamprey attachments and locate the suction area on the sensor in real time.

I. Introduction

Sea lamprey (Petromyzon marinus) is a devastating, invasive fish species in the Laurentian Great Lakes of North America requiring a multimillion-dollar annual control program to protect native fish stocks. A single sea lamprey is capable of consuming 40 lb of host fish during its 12–16-month parasitic life stage [1]. Following the invasion, sea lampreys contributed to the collapse of native fish stocks and of a fishery currently valued at 4.5 billion annually [2]. A binational effort, lead by the Great Lakes Fishery Commission (GLFC), suppresses sea lamprey populations through the use of selective chemical applications [2] and migratory barriers [3], [4], and abundance is monitored through the capture of migratory adults [2]. Migratory barriers block not only invasive species, but desirable species and native species as well resulting in a desire for selective passage [5], [6] around migratory barriers for desirable species. Furthermore, maintaining a network of traps for assessment can be labor-intensive and costly. A means to autonomously detect and monitor sea lamprey could aid with the implementation of selective passage, design of new trapping or population assessment techniques, and help to understand their life history and ecology (e.g., refuge-seeking behavior and habitat characteristics).