Efficient real-time detection of electrical equipment images using a lightweight detector model

Infrared technology holds significant importance in the detection of electrical equipment, as it has the capability to swiftly and securely identify electrical apparatus. To simplify the implementation of proficient detection frameworks for electrical equipment within constrained settings (like embedded apparatus), this study presents an enhanced, lightweight model of the single-shot multibox detector (SSD). This model specifically addresses the detection of multiple equipment objects within infrared imagery. The model realized the lightweight of the model by using the network structure characteristics of squeezenet to modify the backbone network of SSD, and compensated for the impact of the lightweight model on the detection accuracy by adding multiple convolutional layers and connecting branches to enhance the propagation ability and extraction ability of features. To ensure a comprehensive evaluation of the model's detection capabilities, all the models discussed in this study employed the technique of random weight initialization. This approach was utilized to validate the optimal structure of the model and its performance. The experimentation was conducted on both the PASCAL VOC 2007 benchmark dataset and an infrared image dataset encompassing five distinct categories of electrical equipment found within substations. The experimental outcomes indicate that this model offers an efficient approach for achieving lightweight, real-time detection of electrical apparatus.