In order to improve the accuracy and practicability of object detection algorithm in complex environments,an object detection method based on bimodal feature enhancement is proposed by combining multi source information and deep learning technology.This method takes infrared and visible images as input and traditional image processing methods are used such as color space conversion,edge extraction,and histogram equalization to enrich image information and achieve data enhancement effects.In the feature extraction part,the convolutional neural network structure is used to extract the infrared and visible light information of the object respectively,and a hybrid attention mechanism is designed to enhance the effective feature weight from the channel and spatial position respectively.At the same time,an adaptive cross fusion structure is introduced to enhance the feature diversity for the object bimodal information.Finally,the global and local features of the object are fully fused by alternating up and down sampling,and the relevant features of the object are extracted in an autonomous way to achieve detection.The experimental results on standard datasets and the actual real scene datasets show that the proposed method effectively fuses and enhances the multi modal features of the object,improves the object detection effect,and can be better applied to the power grid scene to assist the robot to complete object equipment detection.