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As an important part of flight guarantee service, apron special vehicles have various types and shapes. The existing vehicle detection algorithms suffer from low detection accuracy when identifying special vehicles on the apron and cannot detect when obscured. Aiming at this problem, an algorithm of special vehicle detection based on improved YOLOv5s is proposed. To locate the region of interest quickly and accurately in the detection of special vehicles on the apron, the coordinate attention mechanism is integrated into the backbone network. Considering that the scale of special vehicles varies greatly in the apron monitoring scene, a four-scale feature detection network structure is proposed to enhance the detection ability of special vehicles with different scales. To improve the multi-scale feature fusion capability of the detection network, the neck part of the network is improved by combining the weighted bidirectional feature pyramid structure. The improved algorithm is trained and tested on the self-built apron special vehicle dataset. The experimental results show that compared with YOLOv5s, the precision of the proposed algorithm is improved by 1.6%, the recall is improved by 3.5%, and the average precision mAP0.5 and mAP0.5:0.95 are improved by 2.3% and 3.3%, respectively.