Camouflaged object detection targets at identifying and segmenting objects hidden in the surroundings. Due to the various shapes and sizes, and highly non-discriminative features of camouflaged objects, it is a challenge for Convolutional Neural Networks (CNNs) to detect them from the background. To tackle the first problem of various shapes and sizes, we propose a Scale-Feature Attention (SFA), which can effectively integrate feature information of different scales, so that the model can comprehensively perceive and understand the visual characteristics of different sizes of camouflaged objects. Additionally, the traditional CNN model is difficult to capture the part-whole relationship of camouflaged objects. To solve the second problem of CNNs’ limitation, we propose a Type-Feature Attention (TFA) to integrate contrast from CNNs and part-whole relations from CapsNets, which will improve the identification and object wholeness of camouflaged objects. Experiments on three camouflaged object detection benchmark datasets show that both the proposed SFA and TFA achieve significant performance improvement, which verifies the superiority of the proposed method.