Remainder particles in closed electronic equipment equipped in spacecraft bring huge hidden danger to the flight safety of spacecraft.Since remainder particles are in small size,and even the morphological structure of the re-mainder particles is highly similar to the general components in equipment,and remainder particles are easily covered by other components,the current methods used to detect remainder particles can cause false detection and missed de-tection frequently.To resolve these problems,a Remainder Particle Detection Network(RPDN)was proposed to detect remainder particles in closed electronic equipment based on convolution-inverted residual and combined atten-tion mechanism.A convolution-inverted residual module was built to ensure the integrity of the remainder particles'fine-grained feature.Then,the combined attention mechanism was proposed to enhance the representativeness of re-mainder particles feature.The objects were predicted from multiple dimensions by combining multi-scale feature fu-sion module and object detection layer.The experimental results showed that RPDN had achieved good effect in all evaluation indicators,the mAP of the proposed method reached to 92.16％,and the detection efficiency reached 13FPS.It realized efficient and accurate detection of remainder particles in closed electronic equipment equipped in spacecraft.
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