Early ship fire and smoke detection based on improved YOLOv5
In order to improve the current target detection technology's insufficient accuracy,slow speed and insufficient feature expression ability for early fire smoke detection of small targets in ship environment,the lightweight early ship fire smoke detection method with improved YOLOv5 was studied.Firstly,the network structure of YOLOv5 is analyzed.Secondly,the YOLOv5 algorithm is improved from four aspects:lightweight architecture of backbone network MobileNetv3,improvement of detection neck Bi-FPN feature fusion pyramid,optimization of Alpha-IoU loss function and improvement of Soft-NMS.Finally,ablation and comparison experiments were constructed to verify the improved performance.The training time of the improved model is reduced by 34.1%,and the mAP is im-proved by 6.6%compared with the original model.Its effect on early ship fire smoke detection is excellent,and it has guiding signifi-cance for the early warning of ship fire.
Fire smoke detectionObject detectionAttention mechanismFeature fusionShip safety
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