One of the leading causes of overfishing is the catch of unwanted fish and marine life in commercial fishing gears. Echosounders are nowadays routinely used to detect fish schools and make qualitative estimates of the amount of fish and species present. However, the problem of estimating sizes using acoustic systems is still largely unsolved, with only a few attempts at real-time operation and only at demonstration level. This paper proposes a novel image-based method for individual fish detection, targeted at drastically reducing catches of undersized fish in commercial trawling. The proposal is based on the processing of stereo images acquired by the Deep Vision imaging system, directly placed in the trawl. The images are pre-processed to correct for nonlinearities of the camera response. Then, a Mask R-CNN architecture is used to localize and segment each individual fish in the images. This segmentation is subsequently refined using local gradients to obtain an accurate estimate of the boundary of every fish. Testing was conducted with two representative datasets, containing in excess of 2600 manually annotated individual fish, and acquired using distinct artificial illumination setups. A distinctive advantage of this proposal is the ability to successfully deal with cluttered images containing overlapping fish.

The paper discusses the main methodologies and key technologies behind the ROBUST project, funded by the European Union within the Horizon 2020 framework, whose goal is the development of an autonomous underwater vehicle manipulator system for deep sea mining exploration. The work focuses on the developed control and perception framework and related mechatronics, and presents the experimental results from the sea trials in Sardinia, Italy, supporting the proposed methodologies.

The detection of newly appearing and changing pigmented skin lesions (PSLs) is essential for timely diagnosis of cutaneous melanoma. Total body skin examination (TBSE) procedures, currently practiced for this purpose, can be extremely time-consuming for patients with numerous lesions. In addition, these procedures are prone to subjectivity when selecting PSLs for baseline image comparison, increasing the risk of missing a developing cancer. To address this issue, we propose a new photogrammetry-based total body scanning system allowing for skin surface image acquisition using cross-polarized light. Equipped with 21 high-resolution cameras and a turntable, this scanner automatically acquires a set of overlapping images, covering 85%-90% of the patient's skin surface. These images are used for the automated mapping of PSLs and their change estimation between explorations. The maps produced relate images of individual lesions with their locations on the patient's body, solving the body-to-image and image-to-image correspondence problem in TBSEs. Currently, the scanner is limited to patients with sparse body hair and, for a complete skin examination, the scalp, palms, soles and inner arms should be photographed manually. The initial tests of the scanner showed that it can be successfully applied for automated mapping and temporal monitoring of multiple lesions: PSLs relevant for follow-up were repeatedly mapped in several explorations. Moreover, during the baseline image comparison, all lesions with artificially induced changes were correctly identified as “evolved.”

Total body photography is used for early detection of malignant melanoma, primarily as a means of temporal skin surface monitoring. In a prior work, we presented a scanner with a set of algorithms to map and detect changes in pigmented skin lesions, thus demonstrating that it is possible to fully automate the process of total body image acquisition and processing. The key procedure in these algorithms is skin lesion matching that determines whether two images depict the same real lesion. In this paper, we aim to improve it with respect to false positive and negative outcomes. To this end, we developed two novel methods: one based on successive rigid transformations of threedimensional point clouds and one based on nonrigid coordinate plane deformations in regions of interest around the lesions. In both approaches, we applied a robust outlier rejection procedure based on progressive graph matching. Using the images obtained from the scanner, we created a ground truth dataset tailored to diversify false positive match scenarios. The algorithms were evaluated according to their precision and recall values, and the results demonstrated the superiority of the second approach in all the tests. In the complete interpositional matching experiment, it reached a precision and recall as high as 99.92% and 81.65%, respectively, showing a significant improvement over our original method.

This paper presents the control and perception framework used in the ROBUST EU Horizon 2020 project, aimed at integrate different technologies for developing an underwater autonomous robotic system for exploring deep-sea mining sites. The vehicle firstly collects data of the initial zone of interest and it selects a sub area which is the most probable to contain a manganese nodule field; then it carries out a low altitude survey. When a possible nodule is detected by the cameras, the vehicle lands on the seafloor, allowing the following fixed based manipulation which is designed to perform the nodule analysis. The work reports the implemented control and perception architecture and the preliminary pool experiments results.