  While we enjoy the richness and informativeness of multimodal data, it also
introduces interference and redundancy of information. To achieve optimal
domain interpretation with limited resources, we propose CSDNet, a lightweight
\textbf{C}ross \textbf{S}hallow and \textbf{D}eep Perception \textbf{Net}work
designed to integrate two modalities with less coherence, thereby discarding
redundant information or even modality. We implement our CSDNet for Salient
Object Detection (SOD) task in robotic perception. The proposed method
capitalises on spatial information prescreening and implicit coherence
navigation across shallow and deep layers of the depth-thermal (D-T) modality,
prioritising integration over fusion to maximise the scene interpretation. To
further refine the descriptive capabilities of the encoder for the less-known
D-T modalities, we also propose SAMAEP to guide an effective feature mapping to
the generalised feature space. Our approach is tested on the VDT-2048 dataset,
leveraging the D-T modality outperforms those of SOTA methods using RGB-T or
RGB-D modalities for the first time, achieves comparable performance with the
RGB-D-T triple-modality benchmark method with 5.97 times faster at runtime and
demanding 0.0036 times fewer FLOPs. Demonstrates the proposed CSDNet
effectively integrates the information from the D-T modality. The code will be
released upon acceptance.
