Underwater images are often acquired in sub-optimal lighting conditions, in particular at profound depths where the absence of natural light demands the use of artificial lighting. Low-lighting images impose a challenge for both manual and automated analysis, since regions of interest can have low visibility. A new framework capable of significantly enhancing these images is proposed in this article. The framework is based on a novel dehazing mechanism that considers local contrast information in the input images, and offers a solution to three common disadvantages of current single image dehazing methods: oversaturation of radiance, lack of scale-invariance and creation of halos. A novel low-lighting underwater image dataset, OceanDark, is introduced to assist in the development and evaluation of the proposed framework. Experimental results and a comparison with other underwater-specific image enhancement methods show that the proposed framework can be used for significantly improving the visibility in low-lighting underwater images of different scales, without creating undesired dehazing artifacts.

This review paper offers a contemporary literature survey on symbol spotting in architectural drawing images. Research on isolated symbol recognition is quite mature; the same cannot be said for recognizing a symbol in context. One important challenge is the segmentation/recognition paradox: a system should segment symbols before recognizing them, but some kind of recognition may be necessary to obtain a correct segmentation. Research has thus been recently directed toward symbol spotting, a way of locating possible symbol instances without using full recognition methods. In this paper, we thoroughly review symbol spotting methods with a focus on architectural drawings, an application domain providing the document image analysis and graphic recognition communities with an interesting set of challenges linked to the sheer complexity and density of embedded information, that have yet to be resolved. While most existing methods perform well in terms of recall, their performance is rather poor in terms of precision and false positives. In light of the review, we also propose a simple yet effective symbol spotting method based on template matching and a novel clutter-tolerant cross-correlation function that achieves state-of-the-art results with high precision, high recall, and few false positives, able to cope with “real-life clutter” found in industry-standard architectural drawings.

Echosounders are used by fisheries and ocean observatories, but significant manual effort is required to classify species of interest within multifrequency echograms. This article investigates the use of modified U-Net convolutional neural networks for the pixel-level classification of biological and physical data in echogram images with accurate classification of herring and salmon schools, bubbles, and the sea surface. Data were collected on the coast of British Columbia, Canada, over two years using an Acoustic Zooplankton and Fish Profiler at four frequencies (67, 125, 200, 455 kHz). In addition, simulated data (water depth and solar elevation angle) provide spatial and temporal context to improve the quality of predictions. Redundancy is built into the model by using a tiling strategy during training and classification. During training, using a limited set of annotated data, translational augmentation encodes the U-Nets with robust features that enable applications for alternate deployment configurations (lower sampling rates or alternate water depths). To ensure broad applicability, these networks were trained to classify echograms with noise left intact. The best-performing model classifies herring, salmon, and bubble classes with $\rm {F_{1}}$ scores of 93.0%, 87.3%, and 86.5%, respectively. The results are accurate even when multiple classes are in close proximity, thus, retaining biological data that would otherwise be discarded due to surface bubble noise.

This paper addresses the detection of finfish and krill in echograms. Finfish, in particular Pacific hake, are used both as human food and fish meal. Krill, harvested for aquaculture and aquariums, are a primary food source for finfish, including hake. Thus, spatial distributions of hake follow that of krill. Stock assessments need an accurate differentiation of krill from finfish (hake) in acoustic echograms. This paper proposes a semantic segmentation paradigm for the pixel-level classification of multi-frequency information to detect co-occurring finfish and krill. This paradigm is highly relevant for identifying cloud-like, diffuse krill aggregations that are intertwined with small, often sparse and sometimes dense schools of finfish. We propose U-MSAA-Net, a deep learning U-Net-like framework with novel multi-scale additive attention (MSAA) modules. MSAA modules allow us to leverage all contextual and local information from feature maps available at any given level of the decoding phase of the network, yielding an efficient suppression of the feature responses from regions with lesser semantic value. Experimental results on a new finfish and krill data set spanning across nine months of acoustic data and covering various situations show that U-MSAA-Net outperforms both traditional, texture-based machine learning methods, and deep learning methods based on state-of-the-art semantic segmentation networks. Additional experiments on a data set containing schools of herring and salmon confirm the versatility of U-MSAA-Net and its superiority in terms of accuracy and ability to detect schools of varying sizes. U-MSAA-Net is the first step in creating a comprehensive tool for stock and ecosystem assessments.