Abstract: Object detection in crowded scenarios is crucial in real-time systems, but it faces chal- lenges such as limited hardware resources and occlusion issues, leading to detection delays and reduced accuracy. This paper proposes an occlusion-aware lightweight object detection method (OLODN) comprising 3 parts: a backbone, feature fusion, and output prediction. The method employs fast network blocks for feature extraction and utilizes a positional attention mechanism to focus on occlusion boundaries. The spatial pyramid pooling feature concatenation module in the backbone reduces information loss and enhances the ability to recognize individuals of varying scales and occlusions. The feature fusion section adopts grouped shuffle convolution to optimize feature flow without increasing computational overhead. The output prediction section employs a task-aligned single-stage object detection method to improve recognition accuracy under occlusion conditions. Experimental results show that the method achieves  66.8% recall on the WiderPerson dataset, which is 2.0 percentage points higher than that of YOLOv8-n, with only 1.8×106 model parameters and superior operational efficiency compared to other models. On the Up-Down dataset, the classification error rate and undetected object error rate are 2.6% and 1.3%, respectively, which are 0.4 percentage points and 0.7 percentage points lower than YOLOv8-n. The experiments validate the methods efficiency on resource-constrained devices.

Key words: crowd detection, occlusion detection in human behavior, resource-constrained computing, inter-class and intra-class occlusion, reinforced coordinate attention mechanism, spatial pyramid pooling feature concatenation module