Algorithm for detecting sea cucumbers based on improved SSD

Abstract: Intelligent fishing of sea cucumbers has been an ever-increasing trend using underwater robots in recent aquaculture, instead of the conventional manual operations. However, there is a relatively low distinction between the sea cucumbers and the complex living environment, some of which are semi-hidden in the natural ocean. It is easy to induce the low accuracy of an underwater robot in the recognition of the sea cucumber targets. Particularly, the remote sea cucumbers cannot be recognized as the small targets with the depth of field during the movement of an underwater robot in the natural environment. In this study, the object detection algorithm was proposed for the sea cucumbers using improved Single Shot multibox Detector (SSD) network deep learning. Firstly, the shallow-feature receptive field was improved to increase the location information using a receptive field block. The spatial attention and channel attention mechanisms were then combined to strengthen the features of different depths in the network. The original feature information was multiplied to obtain the weight between each feature channel and feature space. As such, the most representative features were achieved in the channel and spatial feature maps without the irrelevant features. Finally, the fusion of the feature map was performed to further improve the precision of sea cucumber recognition. The actual video was taken to verify the model during testing in the experiment. The improved recognition rate of underwater sea cucumber was obtained at the level of network structure. The experimental results show that the Mean Average Precision (mAP50) was 95.63% for the target detection of sea cucumber using the improved SSD network, and the detection frame rate was 10.7 frame/s. Specifically, the mAP50 increased by 3.85 percentage points, while the detection frame rate was only reduced by 2.8 frame/s, compared with the traditional SSD. The precision-recall (P-R) curves were compared before and after the model improvement. There was a larger area between the P-R curve of the improved SSD model and the X and Y coordinate axes, and the balance point was closer to the coordinates (1, 1), indicating the better performance of the improved SSD model. The Faster R-CNN and YOLOv4 were selected to verify the effectiveness of the improved SSD. The mAP50 values of the improved model were 4.19 and 1.74 percentage points higher than those of the YOLOv4 and Faster R-CNN, respectively, indicating the better system performance of the improved model on the P-R curve than those algorithms. The detection speed of the improved model was 4.6 frame/s lower than that of YOLOv4, whereas, that was 3.95 frame/s higher than that of Faster R-CNN. Consequently, the improved SSD was more suitable for the underwater robot of sea cucumber in the intelligent fishing task, considering the target detection accuracy and running speed. The finding can provide a strong reference for the intelligent fishing of sea cucumbers in aquaculture.