Underwater object detection technology plays an important role in areas of marine resource exploration and environmental protection. However， the problems of blurred imaging and variable object scales in underwater environments pose difficulties for detection tasks. As a result， it is challenging for accurate underwater object feature extraction， which influences the detection performance of existing methods. To solve the above-mentioned problem， a feature enhanced hybrid network was proposed to improve the detection accuracy of underwater objects. Firstly， a global-local hybrid feature enhancement network was constructed. The long-range global information in the image was extracted via self-attention mechanisms， and the richer local detailed information was further calculated through the devised convolutional attention enhancement module. The global and local relationships in the images could be better established， hence the multiscale feature representation powers of the network were enhanced. Subsequently， in order to suppress the interference of imaging blurriness and low contrast in underwater environments， a two-stage object perception enhanced detection head was constructed. The depth of the first-stage region proposal network was increased， thus more semantic information of underwater objects could be extracted. Besides， the self-attention mechanism was introduced in the second stage to suppress the interference from background noise. Moreover， an intersection-over-union branch was added to further integrate the prior information of objects obtained from the first stage into the second stage. The proposed method achieves 37.8%， 61.8%， and 82.0%， 98.9% of mAP0.5:0.95 and AP50 on the TrashCan and WPBB datasets respectively. The qualitative and quantitative comparison experimental results demonstrate that this method could obtain superior detection results for various underwater objects.