Abstract: The present disclosure relates to a small object detection method and apparatus, a readable storage medium, and an electronic device. The method includes: inputting a to-be-detected image to a pre-trained small object detection model; and separately encoding and decoding information of the to-be-detected image in the small object detection model using a desubpixel convolution operation and a subpixel convolution operation running in pair: and extracting features in the to-be-detected image through the small object detection model, and outputting an object's category and location in the to-be-detected image. The present disclosure aims at solving the technical problem in the prior art that traditional FPNs fail to consider the correlation between the downsampling in the backbone network and the upsampling in the neck network during feature fusion, which leads to redundant operations and information loss.

Abstract: An apparatus includes: an input configured to receive an input voltage; an prediction circuit coupled to the input and configured to provide an overcurrent prediction based on analysis of the input voltage; a delay circuit coupled to the input; a gain control circuit coupled to an output of the delay circuit and configured to selectively adjust a gain applied to at least one frequency range of the input voltage based on the overcurrent prediction; a driver coupled to an output of the gain control circuit; and a capacitive load coupled to an output of the driver.