Abstract
Object detection plays a vital role in CCTV systems. Cameras are now deployed at traffic lights, roadways, shopping centers, railways, banks, and other public locations to increase security. It isn’t easy to follow the video quickly and continuously, though. Therefore, surveillance cameras are not required, and human surveillance is needed. A significant difficulty for CCTV cameras is detecting anomalies such as theft, accidents, crimes, and other illegal activities. The frequency of abnormal behaviour is the same as that of regular events. To detect objects in a video, we first analyze each pixel in the image. Segmentation in digital photography is the process of dividing different parts of an image into pixels. Segmentation performance is affected by uneven and dim lighting. These factors significantly impact the realtime object detection process of CCTV systems. In this article, we propose an Adaptive Image Information Enhancement Algorithm (AIIE) to improve images affected by poor lighting. Test results compare the output of the current method with the improved ResNet model architecture and show significant improvement in object detection in video streams. The proposed model delivers better results regarding metrics such as precision, recall, and pixel precision. We also saw
significant improvements in object detection.
Keywords: AIIE, object quality, Low Light, Object Detection, Resnet.