Due to the low high air pressure during the flight, if a fire occurs in the cargo hold of the aircraft, the smoke particles are suspended in mid-air. The traditional smoke detector is difficult to detect, and there is also a high false alarm rate and difficult visualization in other environments, an image-based fire detector was designed, and the improved YOLOv5s algorithm was used to realize the pyrotechnic target detection. First, the backbone network is replaced with a lightweight GhostNet backbone network to facilitate hardware deployment. A collaborative attention module is embedded in the connection between the backbone and the converged network to strengthen the extraction of effective features. Then, according to the development and change characteristics of fire targets, the C3 structure in the feature fusion network was improved, the VoV-GSCSP module was built, and the Slim-ASFF module was embedded between the fusion network and the detection head, so as to jointly strengthen the feature fusion of different scales and realize the further lightweight of the overall network. Finally, the regression loss is replaced by focal EIOU, which solves the problem of penalty term failure and improves the prediction ability of positive samples. The image-based aviation fire detector takes the domestic AI chip RK3588 as the core, connects to the CMOS image sensor for data collection, and realizes information interaction with the airborne display system through the network. The test results show that the equipment can be arranged at the top four corners of the cargo compartment of the simulated aircraft, which can realize the flame alarm within 10 seconds and the smoke alarm within 20 seconds, which provides a feasible solution for ensuring the safety of the aircraft.