Abstract
Accurate poultry detection is crucial for studying poultry behavior using computer vision and video surveillance. However, in free-range farming environments, detecting chickens can often be challenging due to their small size and mutual occlusion. The current detection algorithms exhibit a low level of accuracy, with a high probability of false and missed detections. To address this, we proposed a multi-object chicken detection method named Super-resolution Chicken Detection, which utilizes super-resolution fusion optimization. The algorithm employs the residual-residual dense block to extract image features and used a generative adversarial network to compensate for the loss of details during deep convolution, producing high-resolution images for detection. The proposed algorithm was validated with the B1 data set and the MC1 multi-object data set, demonstrating that the reconstructed images possessed richer pixel features compared to original images, specifically it improved detection accuracy and reduced the number of missed detections. The structural similarity of the reconstructed images was 99.9%, and the peak signal-to-noise ratio was above 30. The algorithm improved the Average Precision50:95 of all You Only Look Once Version X (YOLOX) models, with the largest improvement for the B1 data set with YOLOX-Large (+6.3%) and for the MC1 data set with YOLOX-Small (+4.1%). This was the first time a super-resolution reconstruction technique was applied to multi-object poultry detection. Our method will provide a fresh approach for future poultry researchers to improve the accuracy of object detection using computer vision and video surveillance.