The interaction of hands and tools is the key information to distinguish the behavior of workers. To prevent the errors and omissions of the process in the assembly of pumps and achieve the purpose of real-time monitoring, a workshop tool detection algorithm based on spatial feature fusion is proposed. First, in order to improve the localization ability and detection accuracy of the object, the hand motion region in the foreground is segmented based on the frame difference method to obtain a texture map with hand spatial information, which is combined with RGB images of the assembly process to form a dual channel inputs to the object detection network. The spatial perception module is designed to realize the spatial feature fusion of the dual channel inputs and obtain the global spatial information. The feature enhancement module is proposed to mix the global spatial information and deep semantic information to enhance the feature response at salient locations. Then, the ESNet (enhance shuffleNet) is used to reconstruct the backbone network and form a multi-scale feature extraction module by deep separable convolution to improve the detection speed. Finally, in view of the local elements change both in the foreground and the background, the CutOut data enhancement method is used to improve the anti-interference capability. The experimental results show that the proposed algorithm can effectively reduce the false detection rate, and improve the mAP by 6. 4% compared with the traditional YOLOv5s. The method can quickly and accurately detect the tools used by shop workers.