This paper describes a multi-spectral imaging near infrared (NIR) transflectance system developed for on-line determination of crude chemical composition of highly heterogeneous foods and other bio-materials. The system was evaluated for moisture determination in 70 dried salted coalfish (bacalao), an extremely heterogeneous product. A spectral image cube was obtained for each fish and different sub-sampling approaches for spectral extraction and partial least squares calibration were evaluated. The best prediction models obtained correlation R² values around 0.92 and root mean square error of cross-validation of 0.70%, which is much more accurate than today's traditional manual grading. The combination of non-contact NIR transflectance measurements with spectral imaging allows rather deep penetrating optical sampling as well as large flexibility in spatial sampling patterns and calibration approaches. The technique works well for moisture determination in heterogeneous foods and should, in principle, work for other NIR absorbing compounds such as fat and protein. A part of this study compares the principles of reflectance, contact transflectance and non-contact transflectance with regard to water determination in a set of 20 well-defined dried salted cod samples. Transflectance and non-contact transflectance performed equally well and were superior to reflectance measurements, since the measured light penetrated deeper into the sample.

High-quality video observations are very much needed in underwater environments for the monitoring of several ecosystem indicators and to support the sustainable development and management of almost all activities in the ocean. Reliable video observations are however challenging to collect, because of the generally poor visibility conditions and the difficulties to deploy cost-effective sensors and platforms in the marine environment. Visibility in water is regulated by natural light availability at different depths, and by the presence of suspended particles, scattering incident light in all directions. Those elements are also largely variable in time and space, making it difficult to identify technological solutions that can be used in all conditions. By combining state-of-the-art “time of flight” (ToF) image sensors and innovative pulsed laser illumination, we have developed a range-gated camera system (UTOFIA) that enables affordable and enhanced 3D underwater imaging at high resolution. This range-gated solution allows users to eliminate close-range backscattering, improving quality of the images and providing information on the distance of each illuminated object, hence giving access to real-time 3D measurements. Furthermore, as the system is based on pulsed laser light, it is almost independent of natural light conditions and can achieve similar performances at an extended depth range. We use this system to collect observations in different oceanographic conditions and for different applications, including aquaculture monitoring, seafloor mapping, litter identifications and structure inspection. Performances are evaluated by comparing images to regular cameras and by using standard targets to assess accuracy and precision of distance measurements. We suggest that this type of technology can become a standard in underwater 3D imaging to support the future development of the ocean economy.

We present a range-gated camera system designed for real-time (10 Hz) 3D estimation underwater. The system uses a fast-shutter CMOS sensor ($1280\times 1024$) customized to facilitate gating with 1.67 ns (18.8 cm in water) delay steps relative to the triggering of a solid-state actively $Q$-switched 532 nm laser. A depth estimation algorithm has been carefully designed to handle the effects of light scattering in water, i.e., forward and backward scattering. The raw range-gated signal is carefully filtered to reduce noise while preserving the signal even in the presence of unwanted backscatter. The resulting signal is proportional to the number of photons that are reflected during a small time unit (range), and objects will show up as peaks in the filtered signal. We present a peak-finding algorithm that is robust to unwanted forward scatter peaks and at the same time can pick out distant peaks that are barely higher than peaks caused by sensor and intensity noise. Super-resolution is achieved by fitting a parabola around the peak, which we show can provide depth precision below 1 cm at high signal levels. We show depth estimation results when scanning a range of 8 m (typically 1–9 m) at 10 Hz. The results are dependent on the water quality. We are capable of estimating depth at distances of over 4.5 attenuation lengths when imaging high albedo targets at low attenuation lengths, and we achieve a depth resolution $(\sigma )$ ranging from 0.8 to 9 cm, depending on signal level.

Aquaculture net cage inspection and maintenance is a central issue in fish farming. Inspection using autonomous underwater vehicles is a promising solution. This paper proposes laser-camera triangulation for pose estimation to enable autonomous net following for an autonomous vehicle. The laser triangulation 3D data is experimentally compared to a doppler velocity log (DVL) in an active fish farm. We show that our system is comparable in performance to a DVL for distance and angular pose measurements. Laser triangulation is promising as a short distance ranging sensor for autonomous vehicles at a low cost compared to acoustic sensors.

3D imaging systems provide valuable information for autonomous robot navigation based on landmark detection in pipelines. This paper presents a method for using a time-of-flight (TOF) camera for detection and tracking of pipeline features such as junctions, bends and obstacles. Feature extraction is done by fitting a cylinder to images of the pipeline. Data in captured images appear to take a conic rather than cylindrical shape, and we adjust the geometric primitive accordingly. Pixels deviating from the estimated cylinder/cone fit are grouped into blobs. Blobs fulfilling constraints on shape and stability over time are then tracked. The usefulness of TOF imagery as a source for landmark detection and tracking in pipelines is evaluated by comparison to auxiliary measurements. Experiments using a model pipeline and a prototype robot show encouraging results.

High-precision underwater 3D cameras are required to automate many of the traditional subsea inspection, maintenance and repair (IMR) operations. In this paper we introduce a novel multi-frequency phase stepping (structured light) method for high-precision 3D estimation even in turbid water. We introduce an adaptive phase-unwrapping procedure which uses the phase-uncertainty to determine the highest frequency that can be reliably unwrapped. Light scattering adversely affects the phase estimate. We propose to remove the effect of forward scatter with an unsharp filter and a model-based method to remove the backscatter effect. Tests in varying turbidity show that the scatter correction removes the adverse effect of scatter on the phase estimates. The adaptive frequency unwrapping with scatter correction results in images with higher accuracy and precision and less phase unwrap errors than the Gray-Code Phase Stepping (GCPS) approach.