Automatic Detection of Nephrops Norvegicus Burrows from Underwater Imagery Using Deep Learning

Abstract

The Norway lobster, Nephrops norvegicus, is one of the main commercial crustacean fisheries in Europe. The abundance of Nephrops norvegicus stocks is assessed based on identifying and counting the burrows where they live from underwater videos collected by camera systems mounted on sledges. The Spanish Oceanographic Institute (IEO) and Marine Institute Ireland (MI-Ireland) conducts annual underwater television surveys (UWTV) to estimate the total abundance of Nephrops within the specified area, with a coefficient of variation (CV) or relative standard error of less than 20%. Currently, the identification and counting of the Nephrops burrows are carried out manually by the marine experts. This is quite a time-consuming job. As a solution, we propose an automated system based on deep neural networks that automatically detects and counts the Nephrops burrows in video footage with high precision. The proposed system introduces a deep-learning-based automated way to identify and classify the Nephrops burrows. This research work uses the current state-of-the-art Faster RCNN models Inceptionv2 and MobileNetv2 for object detection and classification. We conduct experiments on two data sets, namely, the Smalls Nephrops survey (FU 22) and Cadiz Nephrops survey (FU 30), collected by Marine Institute Ireland and Spanish Oceanographic Institute, respectively. From the results, we observe that the Inception model achieved a higher precision and recall rate than the MobileNet model. The best mean Average Precision (mAP) recorded by the Inception model is 81.61% compared to MobileNet, which achieves the best mAP of 75.12%.

Dynamic Multiscale Feature Fusion Method for Underwater Target Recognition

“…The results of conventional underwater image recognition are shown in Table 2. In the torpedo type and submarine type, the algorithm in this paper has the highest Ours Literature [33] SA-FPN Journal of Sensors recognition accuracy, which is 0.6715 and 0.9573, respectively. In the frogman type, literature [33] has the highest recognition accuracy of 0.7614, which is slightly higher than that of the algorithm in this paper.…”

“…In the torpedo type and submarine type, the algorithm in this paper has the highest Ours Literature [33] SA-FPN Journal of Sensors recognition accuracy, which is 0.6715 and 0.9573, respectively. In the frogman type, literature [33] has the highest recognition accuracy of 0.7614, which is slightly higher than that of the algorithm in this paper. In the AUV type, the recognition accuracy of SA-FPN is 0.93, which is higher than 0.8732 of the algorithms in this paper.…”

“…In the AUV type, the recognition accuracy of SA-FPN is 0.93, which is higher than 0.8732 of the algorithms in this paper. In terms of recognition speed, literature [33] only needs 0.1 s, which is significantly higher than that of the algorithm in this paper. But the algorithm in this paper has a higher mAP.…”

“…In this paper, multiscale significant features and spatial semantic features are dynamically fused to recognize the target. For underwater weak targets with different interference, three sets of simulation experiments are designed in this section to verify the effectiveness of the proposed algorithm and compare it with FISHnet [30], SiamFPN [31], SA-FPN [32], and literature [33]. The algorithm evaluation criteria are mean average precision (mAP) and recognition time.…”

A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery

“…In our previous work [24], we developed a deep learning model based on state-ofthe-art Faster RCNN [19] models Inceptionv2 [25] and MobileNetv2 [26] for the detection of Nephrops openings. Those models were trained on Gulf of Cadiz and Irish datasets.…”