Currently, the state-of-the-art methods for detecting small unmanned aerial vehicles (UAVs) continue to struggle in complex urban settings due to several persistent challenges, namely, frequent target occlusion, high similarity in thermal radiation signatures between UAVs and their surroundings, and the inherently low visual saliency of small UAV targets, all of which contribute to degraded detection performance. To tackle these issues, this paper introduces a novel multi-modal feature fusion collaborative detection (MFFCD) framework grounded in learnable spatial mapping. The architecture consists of three key components: firstly, a multi-branch parallel feature extraction module (MBPFE) that simultaneously processes infrared, visible, and radar range-azimuth images, complemented by a feature fusion module (FFM) designed to enhance both intra-modal and inter-modal feature interactions; then, an adaptive spatially-aware dynamic detection head module (DDH) that dynamically recalibrates feature weights to strengthen target representation and boost detection accuracy; and a feature collaborative enhancement module (FCE) that employs a learnable affine transformation to align and fuse multi-modal features, thereby producing more robust and reliable detection outcomes. Extensive experiments show that the proposed MFFCD framework substantially outperforms existing methods under challenging urban conditions, achieving a 56.89% gain in Mean Average Precision (mAP) for small UAV detection.