The widespread use of laser line scanners (LLS) aboard unmanned underwater vehicles in the last decade has opened a unique window to a series of ecological and military applications. Variability of underwater light fields and complexity of light contributions reaching the receiver pose a challenge for target detection of LLS under different environmental conditions. The interference of photons not originating at the target (e.g. water path, bottom) can often be minimized (e.g., time-gated systems) but not excluded. Radiative transfer models were developed to better discriminate noise components from signal contributions at the receiver for two continuous LLS: Real-time Ocean Bottom Optical Topographer (ROBOT) and Fluorescence Imaging Laser Line Scanner (FILLS). Numerical experiments using forward Monte Carlo methods were designed to explore the effects of diverse water turbidities and bottom reflectances on ROBOT and FILLS measurements. Interference due to solar light on LLS target detection was also examined. Reliability of radiative transfer models was tested against standard models (Hydrolight) and aquarium measurements. In general a green laser was the best all around choice to detect targets using both LLS sensors. Based on signal-to-noise (S/N) values, performance of ROBOT for target detection was greater (two-fold) than FILLS because of the lower contribution of path photons in ROBOT than FILLS. When ROBOT was located at 1 m above the target, path radiance contributions (noise) were reduced up to 25-fold in clear waters (0.3 mg m-3) with respect to turbid waters (5 mg m-3). Since ROBOT was more discriminative of bottom reflectance discontinuities (high-contrast transitions) than FILLS, algorithms are proposed to retrieve contrasting man-made targets such mines.