Aiming to solve problems of low-accuracy and slow grasp detection in unstructured environments, a grasp detection algorithm alter-attention pyramid network (APNet) is proposed. Generative residual convolutional neural network (GR-ConvNet) was selected as the backbone network, adaptive kernel convolution was used to replace standard convolution, and the SiLU activation function was replaced with the Hardswish activation function. A lightweight feature extraction network was developed, and efficient multiscale attention was introduced to increase focus on important grasping regions. Pyramid convolution was integrated into the residual network to effectively fuse multiscale features. The experimental results demonstrate that APNet achieves 99.3% and 95.8% detection accuracies on the Cornell and Jacquard datasets, with an average time required for single-object detection of 9 ms and 10 ms, respectively. Compared with existing algorithms, APNet demonstrated improved detection performance. In particular, APNet demonstrates an average success rate of 92% on a homemade multi-target dataset for a grasping experiment implemented in a CoppeliaSim simulation environment.