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A Flying Bird Object Detection algorithm Based on Motion Information(FBOD-BMI) is proposed to solve the problem that the features of the object arenot obvious in a single frame, and the size of the object is small (lowSignal-to-Noise Ratio (SNR)) in surveillance video. Firstly, a ConvLSTM-PANmodel structure is designed to capture suspicious flying bird objects, in whichthe Convolutional Long and Short Time Memory (ConvLSTM) network aggregated theSpatio-temporal features of the flying bird object on adjacent multi-framebefore the input of the model and the Path Aggregation Network (PAN) locatedthe suspicious flying bird objects. Then, an object tracking algorithm is usedto track suspicious flying bird objects and calculate their Motion Range (MR).At the same time, the size of the MR of the suspicious flying bird object isadjusted adaptively according to its speed of movement (specifically, if thebird moves slowly, its MR will be expanded according to the speed of the birdto ensure the environmental information needed to detect the flying birdobject). Adaptive Spatio-temporal Cubes (ASt-Cubes) of the flying bird objectsare generated to ensure that the SNR of the flying bird objects is improved,and the necessary environmental information is retained adaptively. Finally, aLightWeight U-Shape Net (LW-USN) based on ASt-Cubes is designed to detectflying bird objects, which rejects the false detections of the suspiciousflying bird objects and returns the position of the real flying bird objects.The monitoring video including the flying birds is collected in the unattendedtraction substation as the experimental dataset to verify the performance ofthe algorithm. The experimental results show that the flying bird objectdetection method based on motion information proposed in this paper caneffectively detect the flying bird object in surveillance video.

Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information