In practical scenarios, detecting and grasping objects accurately can be very challenging due to the uncertainty of their positions and orientations, as well as environmental interference. Especially when the target object is occluded by other objects, traditional machine vision methods have difficulty in accurately recognizing it. To address this problem, we propose the double-attention mechanism-based segmentation grasping detection network (DAM-SGNET). DAM-SGNET is a technique used for detecting and grasping objects accurately in cluttered environments. It utilizes a deep neural network that incorporates two attention mechanisms to predict the optimal grasping posture for RGB images at the pixel level without relying on depth images. The method begins by reannotating datasets, such as the Cornell dataset, cluttered scenes objects dataset, and VMRD dataset, with a new labeling method proposed by previous researchers. These datasets are then used to train an occlusion detection model. DAM-SGNET uses a residual network (SERESNET) with channel attention mechanisms to extract features from the images, and an adaptive decoder including a feature pyramid deformation network and an efficient channel attention module to enhance robustness in cluttered, unstructured open environments. DAM-SGNET ultimately achieves grasp detection accuracy of 99.43%, 99.24%, and 85.38% for the official Cornell grasp dataset, the cluttered scenes grasping dataset, and the VMRD grasping dataset, respectively. Real-world experiments demonstrate the efficacy of DAM-SGNET in self-built robotic arm platforms, achieving a single-target grasping success rate of 99.6%, and an average grasping success rate of 96.46% for cluttered stacked objects.