Lightweight Detection

Lightweight detection focuses on developing computationally efficient object detection models that maintain high accuracy, addressing limitations of resource-intensive deep learning approaches. Current research emphasizes optimizing existing architectures like convolutional neural networks (CNNs) through techniques such as model compression, attention mechanisms, and novel designs like sparsely-connected convolutions, alongside exploring alternative algorithms like gradient boosting. This pursuit is crucial for deploying object detection in resource-constrained environments, such as mobile devices and embedded systems, and for real-time applications requiring rapid processing, impacting diverse fields from agriculture to autonomous vehicles.