These burrows systems have a single or multiple openings or holes with characteristic features that make them different to burrows built for other burrowing species. This species excavates into and inhabits burrow systems mainly in muddy seabed sediments, with more than 40 percent silt and clay. This species is distributed from 10 m to 800 m of depth in the Atlantic NE waters and the Mediterranean Sea, where sediment is suitable for them to construct their burrows. Figure 1 shows the Nephrops norvegicus species (hereafter referred to as Nephrops). One of the most important commercial species in Europe is the Norway lobster Nephrops norvegicus. There are thousands of species in the ocean all over the world. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. To tackle this challenge, we propose a detection refinement algorithm based on spatial–temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention.
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